--- layout: post title: "Extracting data from a variety of Databases/APIs" subtitle: "Showcase of data extraction" date: 2022-08-18 12:55:00 -0400 tags: jupyter_notebook database api background: '/img/posts/database.png' ---
In this post I'll show how to get data from a variety of APIs through Python and how the data can be used to construct pandas dataframes in Python which can then be readily used for subsequent data analysis and visualization. Those last two steps will be the topic of future posts though. APIs enable software components to communicate with each other via a set of definitions and protocols. APIs are a great way to get data from a variety of sources to do research and/or teach yourself things.
Some places have their own libraries/modules specific to their own data. I'll show some examples of how to use them. A great place to find and practice data extraction from APIs is https://rapidapi.com/hub. RapidAPI has a collection of over 7500 APIs that is constantly growing.
#General modules for API extraction
import requests
import json
import pandas as pd
from glom import glom #Can help with extracting deeply nested objects from JSON files
import pprint as pp #Prettifies json output
The API Key and the App ID can be obtained from Nutritionix after you sign up for a develop account. The UPC is a Universal Product Code that identifies a particular product. Here I'll be using a UPC of 706173006046 which corresponds to Natural Value Organic Beans Garbanzo. You can get UPC codes from a variety of sites. I used this site for mine
Once you have those 3 values, the next thing you need is a URL that you can GET a request from. For Nutritionix the URL has the following base structure:
https://api.nutritionix.com/v1_1/item?upc=[UPC]&appId=[APPID]&appKey=[APIKEY]
I like setting up my URLs as formatted strings since it allows me to readily change my query, app_id, and/or api_key if/as needed
#Nutritionix
nix_apikey = '0cfcb1e3abae28de2ec81c3fccb6c7ea'
upc = '706173006046'
nix_appid = 'addc9626'
url = f'https://api.nutritionix.com/v1_1/item?upc={upc}&appId={nix_appid}&appKey={nix_apikey}'
Now that we have those elements, we can start requesting data using requests
r = requests.get(url)
r
<Response [200]>
If your r value turns out to be 400 or 404 then chances are that the UPC code you entered is invalid. If this doesn't work you may want to try the EAN-13 code instead. Next, let's unpack this data using JSON.
json_dict = r.json()
json_dict
{'old_api_id': None,
'item_id': '59841cec3695799351031983',
'item_name': 'Garbanzo Beans, Organic',
'leg_loc_id': None,
'brand_id': '51db37b1176fe9790a8984af',
'brand_name': 'Natural Value',
'item_description': None,
'updated_at': '2017-08-04T07:06:33.000Z',
'nf_ingredient_statement': None,
'nf_water_grams': None,
'nf_calories': 130,
'nf_calories_from_fat': 10,
'nf_total_fat': 1,
'nf_saturated_fat': 0,
'nf_trans_fatty_acid': 0,
'nf_polyunsaturated_fat': None,
'nf_monounsaturated_fat': None,
'nf_cholesterol': 0,
'nf_sodium': 90,
'nf_total_carbohydrate': 19,
'nf_dietary_fiber': 4,
'nf_sugars': 1,
'nf_protein': 5,
'nf_vitamin_a_dv': 0,
'nf_vitamin_c_dv': 2,
'nf_calcium_dv': 2,
'nf_iron_dv': 8,
'nf_refuse_pct': None,
'nf_servings_per_container': 3.5,
'nf_serving_size_qty': 0.5,
'nf_serving_size_unit': 'cup',
'nf_serving_weight_grams': 130,
'allergen_contains_milk': None,
'allergen_contains_eggs': None,
'allergen_contains_fish': None,
'allergen_contains_shellfish': None,
'allergen_contains_tree_nuts': None,
'allergen_contains_peanuts': None,
'allergen_contains_wheat': None,
'allergen_contains_soybeans': None,
'allergen_contains_gluten': None,
'usda_fields': None}
Let's take a look at some of the keys we have access to for this product
json_dict.keys()
dict_keys(['old_api_id', 'item_id', 'item_name', 'leg_loc_id', 'brand_id', 'brand_name', 'item_description', 'updated_at', 'nf_ingredient_statement', 'nf_water_grams', 'nf_calories', 'nf_calories_from_fat', 'nf_total_fat', 'nf_saturated_fat', 'nf_trans_fatty_acid', 'nf_polyunsaturated_fat', 'nf_monounsaturated_fat', 'nf_cholesterol', 'nf_sodium', 'nf_total_carbohydrate', 'nf_dietary_fiber', 'nf_sugars', 'nf_protein', 'nf_vitamin_a_dv', 'nf_vitamin_c_dv', 'nf_calcium_dv', 'nf_iron_dv', 'nf_refuse_pct', 'nf_servings_per_container', 'nf_serving_size_qty', 'nf_serving_size_unit', 'nf_serving_weight_grams', 'allergen_contains_milk', 'allergen_contains_eggs', 'allergen_contains_fish', 'allergen_contains_shellfish', 'allergen_contains_tree_nuts', 'allergen_contains_peanuts', 'allergen_contains_wheat', 'allergen_contains_soybeans', 'allergen_contains_gluten', 'usda_fields'])
Let's access some info from this dictionary
print(json_dict['brand_name'],'\n',
json_dict['nf_calories'],'\n',
json_dict['nf_sodium'],'\n',
json_dict['nf_sugars'],'\n',
json_dict['nf_iron_dv'])
Natural Value 130 90 1 8
Cool! We can get all sorts of info for a variety of foods this way. There is a wrapper for the Nutritionix database that can facilitate operations to get queries in a much more streamlined fasion. We can use it as follows:
from nutritionix import Nutritionix #Nutritionix database for nutrition info
nix = Nutritionix(app_id = nix_appid, api_key = nix_apikey)
results = nix.search('Garbanzo').json()
results
{'total_hits': 514,
'max_score': 13.36457,
'hits': [{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '513fceb675b8dbbc210021c8',
'_score': 13.36457,
'fields': {'item_id': '513fceb675b8dbbc210021c8',
'item_name': 'Garbanzo Beans - 1 cup',
'brand_name': 'USDA',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '60a7bb52805e4a2d62e7842e',
'_score': 4.7935605,
'fields': {'item_id': '60a7bb52805e4a2d62e7842e',
'item_name': 'Garbanzo',
'brand_name': 'SCN Sales',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '550d8dfb1abac49e69394465',
'_score': 4.62599,
'fields': {'item_id': '550d8dfb1abac49e69394465',
'item_name': 'Garbanzo',
'brand_name': 'The Fresh Market',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '55dc291973184b617abd9eaf',
'_score': 4.580312,
'fields': {'item_id': '55dc291973184b617abd9eaf',
'item_name': 'Garbanzo',
'brand_name': 'Sadaf',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '59dc70293b737c9301da9545',
'_score': 4.580312,
'fields': {'item_id': '59dc70293b737c9301da9545',
'item_name': 'Garbanzo',
'brand_name': 'Montjardin',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '529e7f31f9655f6d350088bd',
'_score': 4.36608,
'fields': {'item_id': '529e7f31f9655f6d350088bd',
'item_name': 'Garbanzo Seasoned Chips',
'brand_name': 'Garbanzo',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '529e7f31ea63d49335008954',
'_score': 4.037829,
'fields': {'item_id': '529e7f31ea63d49335008954',
'item_name': 'Tahini Sauce, Entree Salad',
'brand_name': 'Garbanzo',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '529e7f31ea63d49335008959',
'_score': 4.037829,
'fields': {'item_id': '529e7f31ea63d49335008959',
'item_name': 'White Milk, 1%',
'brand_name': 'Garbanzo',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '529e7f31f9655f6d3500889e',
'_score': 4.037829,
'fields': {'item_id': '529e7f31f9655f6d3500889e',
'item_name': 'Chicken Kabob',
'brand_name': 'Garbanzo',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '23cb91820f531c9059752f15',
'_score': 4.037829,
'fields': {'item_id': '23cb91820f531c9059752f15',
'item_name': 'Rice, Pita',
'brand_name': 'Garbanzo',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}}]}
We can also have it return just the first item as follows:
nix.search('Garbanzo', results="0:1").json()
{'total_hits': 513,
'max_score': 13.48859,
'hits': [{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'item',
'_id': '513fceb675b8dbbc210021c8',
'_score': 13.48859,
'fields': {'item_id': '513fceb675b8dbbc210021c8',
'item_name': 'Garbanzo Beans - 1 cup',
'brand_name': 'USDA',
'nf_serving_size_qty': 1,
'nf_serving_size_unit': 'serving'}}]}
We can also search by brands by using the following notation
nix.brand().search(query="chipotle").json()
{'total': 3,
'max_score': 11.98567,
'hits': [{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'brand',
'_id': '513fbc1283aa2dc80c000002',
'_score': 11.98567,
'fields': {'name': 'Chipotle',
'website': 'http://www.chipotle.com',
'type': 1,
'_id': '513fbc1283aa2dc80c000002'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'brand',
'_id': '561c00871485de5737d086dd',
'_score': 7.4692764,
'fields': {'name': 'Chicken Chipotle',
'website': None,
'type': 2,
'_id': '561c00871485de5737d086dd'}},
{'_index': 'f762ef22-e660-434f-9071-a10ea6691c27',
'_type': 'brand',
'_id': '54c751ece1a9773d18e42bd8',
'_score': 7.3735495,
'fields': {'name': "Smokin' Chipotle",
'website': None,
'type': 2,
'_id': '54c751ece1a9773d18e42bd8'}}]}
Let's now load this json data into a pandas dataframe. Since the json data we got is in a nested list format, we need to use the json_normalize function.
results = nix.search('Garbanzo, Apple').json()
print(type(results['hits']))
df = pd.json_normalize(results)
df
<class 'list'>
| total_hits | max_score | hits | |
|---|---|---|---|
| 0 | 18317 | 2.651146 | [{'_index': 'f762ef22-e660-434f-9071-a10ea6691... |
This is not quite what we want though. However, we can include the record_path parameter as part of our input and set it to the hits column to fix this.
results = nix.search('Garbanzo').json()
df = pd.json_normalize(results, record_path = ['hits'])
df.head()
| _index | _type | _id | _score | fields.item_id | fields.item_name | fields.brand_name | fields.nf_serving_size_qty | fields.nf_serving_size_unit | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 513fceb675b8dbbc210021c8 | 13.503290 | 513fceb675b8dbbc210021c8 | Garbanzo Beans - 1 cup | USDA | 1 | serving |
| 1 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 60a7bb52805e4a2d62e7842e | 4.844969 | 60a7bb52805e4a2d62e7842e | Garbanzo | SCN Sales | 1 | serving |
| 2 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 550d8dfb1abac49e69394465 | 4.642818 | 550d8dfb1abac49e69394465 | Garbanzo | The Fresh Market | 1 | serving |
| 3 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 55dc291973184b617abd9eaf | 4.625291 | 55dc291973184b617abd9eaf | Garbanzo | Sadaf | 1 | serving |
| 4 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 59dc70293b737c9301da9545 | 4.625291 | 59dc70293b737c9301da9545 | Garbanzo | Montjardin | 1 | serving |
Here's an example of we can process multiple queries programatically
# Make a food list
food = ['waffle', 'apple', 'cottage', 'cheese', 'muffin', 'soup', 'coffee', 'butter', 'ham',
'syrup', 'tortilla','salt', 'pepper', 'butternut', 'walnut', 'cinnamon', 'kale', 'spinach',
'banana', 'bean', 'turkey', 'jam','carrot', 'cracker', 'rice', 'soy', 'tempura', 'sushi', 'orange',
'pasta', 'chicken', 'beef', 'fish', 'lamb','pork', 'broccoli', 'lettuce', 'tomato', 'garlic',
'bacon', 'salad', 'vanilla', 'raspberry','cake', 'beer','green', 'onion', 'vegetable', 'chocolate',
'sugar','chip', 'cookie', 'milk', 'cashew', 'vinegar', 'artichoke','cheddar', 'cauliflower',
'pinto','egg', 'peanut', 'cocoa', 'avocado', 'pomegranate','clementine', 'grape']
#Initiate list to hold generated dataframes
df_list = []
for elem in food:
results = nix.search(elem).json()
df = pd.json_normalize(results, record_path = ['hits'])
df_list.append(df)
final_df = pd.concat(df_list, ignore_index = True)
print(len(food))
final_df
66
| _index | _type | _id | _score | fields.item_id | fields.item_name | fields.brand_name | fields.nf_serving_size_qty | fields.nf_serving_size_unit | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 513fceb775b8dbbc2100294d | 13.242171 | 513fceb775b8dbbc2100294d | Waffle - 1 waffle, round (7" dia) | USDA | 1 | serving |
| 1 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 5900a1ef7924e49b03571ec5 | 12.982630 | 5900a1ef7924e49b03571ec5 | Waffle - 1 waffle, round (8" dia) | USDA | 1 | serving |
| 2 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 513fceb775b8dbbc2100294e | 12.897008 | 513fceb775b8dbbc2100294e | Waffle - 1 oz | USDA | 1 | serving |
| 3 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 5755d4a47faba81b4d8c87ee | 4.631118 | 5755d4a47faba81b4d8c87ee | Waffle | Kingsmill | 1 | serving |
| 4 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 5701fc820af88e5e7a66f7a1 | 4.509888 | 5701fc820af88e5e7a66f7a1 | Waffle | Giant Eagle | 1 | serving |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 655 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 513fceb575b8dbbc21001052 | 11.694013 | 513fceb575b8dbbc21001052 | Grapes - 1 cup | USDA | 1 | serving |
| 656 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 5d76608c34d5374445dc6fb2 | 11.637632 | 5d76608c34d5374445dc6fb2 | Grapes, raw - 1 oz | USDA | 1 | serving |
| 657 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 5589f586c1aa12d129a28017 | 4.571935 | 5589f586c1aa12d129a28017 | Grape | Funnbar | 1 | serving |
| 658 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 6141fb50615135000990dd85 | 4.571935 | 6141fb50615135000990dd85 | Grape | Torani | 1 | serving |
| 659 | f762ef22-e660-434f-9071-a10ea6691c27 | item | 5bd2bc6fa8cc29736deb742f | 4.158502 | 5bd2bc6fa8cc29736deb742f | Grape | Pollapo | 1 | serving |
660 rows × 9 columns
Awesome! For some reason though I'm only getting at most 10 results from this process even though the query says that there are more... I'll have to investigate that further but it could probably be resolved by submitting queries within a loop to get all the values
The World Bank has data that you can find here. All you need is a URL and a properly structured search query using indicators to add to your URL.Once you have that, you can use the same requests that we used before. You can find examples of different API Call Structures for this database here
The world_bank_data module has all the data we need so all it requires is for us to call it. You can get more info here https://pypi.org/project/world-bank-data/. The nice thing about this module is that it places your request into a dataframe so there's no need to do additional coding to get our data ready.
We can get information from a variety of topics like Agriculture, Education, Poverty, and Climate Change as shown below
import world_bank_data as wb #This is the database for the World Bank
wb.get_topics()
| value | sourceNote | |
|---|---|---|
| id | ||
| 1 | Agriculture & Rural Development | For the 70 percent of the world's poor who liv... |
| 2 | Aid Effectiveness | Aid effectiveness is the impact that aid has i... |
| 3 | Economy & Growth | Economic growth is central to economic develop... |
| 4 | Education | Education is one of the most powerful instrume... |
| 5 | Energy & Mining | The world economy needs ever-increasing amount... |
| 6 | Environment | Natural and man-made environmental resources –... |
| 7 | Financial Sector | An economy's financial markets are critical to... |
| 8 | Health | Improving health is central to the Millennium ... |
| 9 | Infrastructure | Infrastructure helps determine the success of ... |
| 10 | Social Protection & Labor | The supply of labor available in an economy in... |
| 11 | Poverty | For countries with an active poverty monitorin... |
| 12 | Private Sector | Private markets drive economic growth, tapping... |
| 13 | Public Sector | Effective governments improve people's standar... |
| 14 | Science & Technology | Technological innovation, often fueled by gove... |
| 15 | Social Development | Data here cover child labor, gender issues, re... |
| 16 | Urban Development | Cities can be tremendously efficient. It is ea... |
| 17 | Gender | Gender equality is a core development objectiv... |
| 18 | Millenium development goals | |
| 19 | Climate Change | Climate change is expected to hit developing c... |
| 20 | External Debt | Debt statistics provide a detailed picture of ... |
| 21 | Trade | Trade is a key means to fight poverty and achi... |
The WBDB is obtained from a variety of sources. We can get information about what those sources are as shown below.
wb.get_sources()
| lastupdated | name | code | description | url | dataavailability | metadataavailability | concepts | |
|---|---|---|---|---|---|---|---|---|
| id | ||||||||
| 1 | 2021-08-18 | Doing Business | DBS | Y | Y | 3 | ||
| 2 | 2022-07-20 | World Development Indicators | WDI | Y | Y | 3 | ||
| 3 | 2021-09-27 | Worldwide Governance Indicators | WGI | Y | Y | 3 | ||
| 5 | 2016-03-21 | Subnational Malnutrition Database | SNM | Y | Y | 3 | ||
| 6 | 2022-01-14 | International Debt Statistics | IDS | Y | Y | 4 | ||
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 84 | 2022-07-19 | Education Policy | EDP | Y | Y | 3 | ||
| 85 | 2022-07-06 | PEFA_2021_SNG | SNG | Y | N | 4 | ||
| 86 | 2021-09-24 | Global Jobs Indicators Database (JOIN) | JON | Y | Y | 3 | ||
| 87 | 2022-06-10 | Country Climate and Development Report (CCDR) | CCD | Y | Y | 3 | ||
| 88 | 2022-07-05 | Food Prices for Nutrition | FPN | Y | Y | 4 |
68 rows × 8 columns
We can also get a variety of information about countries
countries = wb.get_countries()
countries
| iso2Code | name | region | adminregion | incomeLevel | lendingType | capitalCity | longitude | latitude | |
|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||
| ABW | AW | Aruba | Latin America & Caribbean | High income | Not classified | Oranjestad | -70.0167 | 12.5167 | |
| AFE | ZH | Africa Eastern and Southern | Aggregates | Aggregates | Aggregates | NaN | NaN | ||
| AFG | AF | Afghanistan | South Asia | South Asia | Low income | IDA | Kabul | 69.1761 | 34.5228 |
| AFR | A9 | Africa | Aggregates | Aggregates | Aggregates | NaN | NaN | ||
| AFW | ZI | Africa Western and Central | Aggregates | Aggregates | Aggregates | NaN | NaN | ||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| XZN | A5 | Sub-Saharan Africa excluding South Africa and ... | Aggregates | Aggregates | Aggregates | NaN | NaN | ||
| YEM | YE | Yemen, Rep. | Middle East & North Africa | Middle East & North Africa (excluding high inc... | Low income | IDA | Sana'a | 44.2075 | 15.3520 |
| ZAF | ZA | South Africa | Sub-Saharan Africa | Sub-Saharan Africa (excluding high income) | Upper middle income | IBRD | Pretoria | 28.1871 | -25.7460 |
| ZMB | ZM | Zambia | Sub-Saharan Africa | Sub-Saharan Africa (excluding high income) | Low income | IDA | Lusaka | 28.2937 | -15.3982 |
| ZWE | ZW | Zimbabwe | Sub-Saharan Africa | Sub-Saharan Africa (excluding high income) | Lower middle income | Blend | Harare | 31.0672 | -17.8312 |
299 rows × 9 columns
There's a ton of information we can get from this database via indicators. You can get the list of indicators as shown below.
indicators = wb.get_indicators()
indicators
| name | unit | source | sourceNote | sourceOrganization | topics | |
|---|---|---|---|---|---|---|
| id | ||||||
| 1.0.HCount.1.90usd | Poverty Headcount ($1.90 a day) | LAC Equity Lab | The poverty headcount index measures the propo... | LAC Equity Lab tabulations of SEDLAC (CEDLAS a... | Poverty | |
| 1.0.HCount.2.5usd | Poverty Headcount ($2.50 a day) | LAC Equity Lab | The poverty headcount index measures the propo... | LAC Equity Lab tabulations of SEDLAC (CEDLAS a... | Poverty | |
| 1.0.HCount.Mid10to50 | Middle Class ($10-50 a day) Headcount | LAC Equity Lab | The poverty headcount index measures the propo... | LAC Equity Lab tabulations of SEDLAC (CEDLAS a... | Poverty | |
| 1.0.HCount.Ofcl | Official Moderate Poverty Rate-National | LAC Equity Lab | The poverty headcount index measures the propo... | LAC Equity Lab tabulations of data from Nation... | Poverty | |
| 1.0.HCount.Poor4uds | Poverty Headcount ($4 a day) | LAC Equity Lab | The poverty headcount index measures the propo... | LAC Equity Lab tabulations of SEDLAC (CEDLAS a... | Poverty | |
| ... | ... | ... | ... | ... | ... | ... |
| WP15163_4.3 | Mobile account, female (% age 15+) | WDI Database Archives | Mobile account denotes the percentage of respo... | Demirguc-Kunt et al., 2015, Global Financial I... | Financial Sector ,Gender | |
| WP15163_4.8 | Mobile account, income, poorest 40% (% ages 15+) | WDI Database Archives | Mobile account denotes the percentage of respo... | Demirguc-Kunt et al., 2015, Global Financial I... | Financial Sector | |
| WP15163_4.9 | Mobile account, income, richest 60% (% ages 15+) | WDI Database Archives | Mobile account denotes the percentage of respo... | Demirguc-Kunt et al., 2015, Global Financial I... | Financial Sector | |
| wpremia_F | Wage Premia for Females (the ratio of skilled ... | Gender Disaggregated Labor Database (GDLD) | ||||
| wpremia_M | Wage Premia for Males (the ratio of skilled m... | Gender Disaggregated Labor Database (GDLD) |
20256 rows × 6 columns
We can get a list of topics that start with a certain letter using the following code:
new_df = indicators.loc[indicators['topics'].str[0].isin(['S']), 'topics']
print(type(new_df))
new_df
<class 'pandas.core.series.Series'>
id
4.0.nini.15a18 Social Protection & Labor
4.0.nini.15a24 Social Protection & Labor
4.0.nini.19a24 Social Protection & Labor
4.0.stud.15a18 Social Protection & Labor
4.0.stud.15a24 Social Protection & Labor
...
SL.WAG.0714.FE.ZS Social Protection & Labor
SL.WAG.0714.MA.ZS Social Protection & Labor
SL.WAG.0714.ZS Social Protection & Labor
SP.POP.SCIE.RD.P6 Science & Technology
SP.POP.TECH.RD.P6 Science & Technology
Name: topics, Length: 2141, dtype: object
The method above returns a 'pandas.core.series.Series' type object. We can place it into a dataframe again using the to_frame method
final_df = new_df.to_frame()
final_df
| topics | |
|---|---|
| id | |
| 4.0.nini.15a18 | Social Protection & Labor |
| 4.0.nini.15a24 | Social Protection & Labor |
| 4.0.nini.19a24 | Social Protection & Labor |
| 4.0.stud.15a18 | Social Protection & Labor |
| 4.0.stud.15a24 | Social Protection & Labor |
| ... | ... |
| SL.WAG.0714.FE.ZS | Social Protection & Labor |
| SL.WAG.0714.MA.ZS | Social Protection & Labor |
| SL.WAG.0714.ZS | Social Protection & Labor |
| SP.POP.SCIE.RD.P6 | Science & Technology |
| SP.POP.TECH.RD.P6 | Science & Technology |
2141 rows × 1 columns
We can also search for a particular topic via the following code where I'm looking for indicators associated with Climate Change.
cc = wb.get_indicators(topic=19)
cc.head()
| name | unit | source | sourceNote | sourceOrganization | topics | |
|---|---|---|---|---|---|---|
| id | ||||||
| AG.LND.AGRI.K2 | Agricultural land (sq. km) | World Development Indicators | Agricultural land refers to the share of land ... | Food and Agriculture Organization, electronic ... | Agriculture & Rural Development ,Climate Change | |
| AG.LND.AGRI.ZS | Agricultural land (% of land area) | World Development Indicators | Agricultural land refers to the share of land ... | Food and Agriculture Organization, electronic ... | Agriculture & Rural Development ,Climate Chan... | |
| AG.LND.ARBL.ZS | Arable land (% of land area) | World Development Indicators | Arable land includes land defined by the FAO a... | Food and Agriculture Organization, electronic ... | Agriculture & Rural Development ,Climate Chan... | |
| AG.LND.EL5M.RU.K2 | Rural land area where elevation is below 5 met... | World Development Indicators | Rural land area below 5m is the total rural la... | Center for International Earth Science Informa... | Agriculture & Rural Development ,Climate Chan... | |
| AG.LND.EL5M.RU.ZS | Rural land area where elevation is below 5 met... | World Development Indicators | Rural land area below 5m is the percentage of ... | Center for International Earth Science Informa... | Agriculture & Rural Development ,Climate Chan... |
To get information about population we can enter the following indicator 'SP.POP.TOTL'
# Population dataset, by the World Bank (most recent value)
population = wb.get_series('SP.POP.TOTL')
population
Country Series Year
Africa Eastern and Southern Population, total 1960 130836765.0
1961 134159786.0
1962 137614644.0
1963 141202036.0
1964 144920186.0
...
Zimbabwe Population, total 2017 14236599.0
2018 14438812.0
2019 14645473.0
2020 14862927.0
2021 15092171.0
Name: SP.POP.TOTL, Length: 16492, dtype: float64
There's a whole lot more information we can extract and I'll play with this database more in a future post. You can of course always use the standard requests method, just make sure you have a URL and something to search.
Once you have that setup, you'll want to also install the spotipy API which you can do using pip install spotipy.
Then we'll import spotipy and set our credentials so that we can start querying the API endpoints as follows:
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
cid = '4a560a9a37c54f5ca18aff5b645c1736'
secret = '0d5fa5169ea34753ae2c70a4b751db51'
s_uri = 'http://localhost/'
client_credentials_manager = SpotifyClientCredentials(client_id=cid, client_secret=secret)
sp = spotipy.Spotify(client_credentials_manager = client_credentials_manager)
Now we should be ready to start retrieving data from Spotify! There's a variety of ways to extract info, I'll cover a few here but you can also find the docs here: https://spotipy.readthedocs.io/en/master/#getting-started
Let's start by finding album info from a specific artist. Here I'll use The Smashing Pumpkins. To do this all we need is the URI for them in Spotify. If you are on Windows, you can get the URI by:
CTRL key in your keyboard. The text will now say 'Copy Spotify URI'For The Smashing Pumpkins, the Spotify URI link is: spotify:artist:40Yq4vzPs9VNUrIBG5Jr2i
Once you have that you can run the following code:
tsp_uri = 'spotify:artist:40Yq4vzPs9VNUrIBG5Jr2i'
results = sp.artist_albums(tsp_uri, album_type='album')
albums = results['items']
while results['next']:
results = sp.next(results)
albums.extend(results['items'])
for album in albums:
print(album['name'])
CYR SHINY AND OH SO BRIGHT, VOL. 1 / LP: NO PAST. NO FUTURE. NO SUN. SHINY AND OH SO BRIGHT, VOL. 1 / LP: NO PAST. NO FUTURE. NO SUN. SHINY AND OH SO BRIGHT, VOL. 1 / LP: NO PAST. NO FUTURE. NO SUN. Monuments to an Elegy Monuments to an Elegy Monuments To An Elegy Oceania Oceania Oceania Oceania Oceania Oceania Machina / The Machines Of God Adore (Super Deluxe) Mellon Collie And The Infinite Sadness (Deluxe Edition) Pisces Iscariot (Deluxe) Siamese Dream (2011 - Remaster) Siamese Dream (Deluxe Edition) Gish (Deluxe Edition)
We can also get album covers for the top 5 tracks using the following method:
import urllib.request
import cv2
import numpy as np
import matplotlib.pyplot as plt
results = sp.artist_top_tracks(tsp_uri)
fig, axs = plt.subplots(1, 5, figsize = (20,20))
i = 0
for track in results['tracks'][:5]:
print('track : ' + track['name'])
print('cover art: ' + track['album']['images'][0]['url'])
#This is just to display the album covers in the output
req = urllib.request.urlopen(track['album']['images'][0]['url'])
arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
img = cv2.imdecode(arr, -1) # 'Load it as it is'
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
axs[i].imshow(img)
axs[i].title.set_text(track['name'])
i += 1
print()
plt.show()
track : 1979 - Remastered 2012 cover art: https://i.scdn.co/image/ab67616d0000b273431ac6e6f393acf475730ec6 track : Bullet With Butterfly Wings - Remastered 2012 cover art: https://i.scdn.co/image/ab67616d0000b273431ac6e6f393acf475730ec6 track : Zero - Remastered 2012 cover art: https://i.scdn.co/image/ab67616d0000b273431ac6e6f393acf475730ec6 track : Tonight, Tonight - Remastered 2012 cover art: https://i.scdn.co/image/ab67616d0000b273431ac6e6f393acf475730ec6 track : Today - 2011 Remaster cover art: https://i.scdn.co/image/ab67616d0000b2735274788f34fc7656d2856dfd
Here I'll show how you can query for multiple artists and find their track names, popularity score, and track IDs. This example is for 1000 artists. sp.search only returns 50 results at most per query, so in order to get more results, the query needs to be enclosed inside a loop.
artist_name = []
track_name = []
popularity = []
track_id = []
for i in range(0,1000,50):
track_results = sp.search(q = 'year:2021', type = 'track', limit = 50, offset = i)
for i, t in enumerate(track_results['tracks']['items']):
artist_name.append(t['artists'][0]['name'])
track_name.append(t['name'])
track_id.append(t['id'])
popularity.append(t['popularity'])
For easier viewing, I'll place the results of our query into a dataframe. The dataframe will have four columns, corresponding to each of the things we queried for
track_dataframe = pd.DataFrame({'artist_name' : artist_name,
'track_name' : track_name,
'track_id' : track_id,
'popularity' : popularity})
track_dataframe
| artist_name | track_name | track_id | popularity | |
|---|---|---|---|---|
| 0 | Nicky Youre | Sunroof | 4h4QlmocP3IuwYEj2j14p8 | 91 |
| 1 | Dj Biffen | Merry Christmas and Happy New Year 2021 - Radi... | 7h7yPyss4MIBVPXVeF83vg | 18 |
| 2 | Morgan Wallen | Wasted On You | 3cBsEDNhFI9E82vPj3kvi3 | 82 |
| 3 | Elvis Costello & The Attractions | Pump It Up - 2021 Remaster | 3oyc1mIdCBGaU55wX7otqM | 61 |
| 4 | Drake | Knife Talk (with 21 Savage ft. Project Pat) | 2BcMwX1MPV6ZHP4tUT9uq6 | 84 |
| ... | ... | ... | ... | ... |
| 995 | Armin van Buuren | For All Time | 17lMWEUwogW3qRTZipD12p | 12 |
| 996 | Andy Grammer | Lease On Life | 4ufkuONjQMNR2fyXu1bO9w | 69 |
| 997 | Tinlicker | Children | 4BdZyQpNHXlvK4co5qV3So | 18 |
| 998 | Florida Georgia Line | Long Live | 0YfRAYTNTNszGg8VARrSIH | 66 |
| 999 | Funcc. | i still remember toronto | 5jNWFTEa6WYX04E5XRgSVt | 6 |
1000 rows × 4 columns
Cool! Now we can start doing more stuff with the data. I'll do a project with this in a future post.
To start we need to determine which tables contain the data we are interested in. We do this by using the search function which takes the following form:
censusdata.variable_info.search(src, year, field, criterion, tabletype='detail')
This function returns a list of tuples that have names, concepts and labels that match our search criterion.
In this example I'll show how to find the status of science and education in Hudson County, NJ which contains Jersey City.
I'll start by making a query for science on the year 2019 using the AC5 database. You'll have to play with the search ranges a bit to find data that seems interesting/relevant to you.
from census import Census
import censusdata
from us import states
#Show first 10 results of search
censusdata.search('acs5', 2019, 'label', 'science')[101:200]
[('B24022_006E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Management, business, and financial occupations:!!Business and financial operations occupations'),
('B24022_007E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Computer, engineering, and science occupations:'),
('B24022_008E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Computer, engineering, and science occupations:!!Computer and mathematical occupations'),
('B24022_009E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Computer, engineering, and science occupations:!!Architecture and engineering occupations'),
('B24022_010E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Computer, engineering, and science occupations:!!Life, physical, and social science occupations'),
('B24022_011E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Education, legal, community service, arts, and media occupations:'),
('B24022_012E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Education, legal, community service, arts, and media occupations:!!Community and social service occupations'),
('B24022_013E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Education, legal, community service, arts, and media occupations:!!Legal occupations'),
('B24022_014E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Education, legal, community service, arts, and media occupations:!!Educational instruction, and library occupations'),
('B24022_015E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Education, legal, community service, arts, and media occupations:!!Arts, design, entertainment, sports, and media occupations'),
('B24022_016E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Healthcare practitioners and technical occupations:'),
('B24022_017E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Healthcare practitioners and technical occupations:!!Health diagnosing and treating practitioners and other technical occupations'),
('B24022_018E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Management, business, science, and arts occupations:!!Healthcare practitioners and technical occupations:!!Health technologists and technicians'),
('B24022_039E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:'),
('B24022_040E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Management, business, and financial occupations:'),
('B24022_041E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Management, business, and financial occupations:!!Management occupations'),
('B24022_042E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Management, business, and financial occupations:!!Business and financial operations occupations'),
('B24022_043E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Computer, engineering, and science occupations:'),
('B24022_044E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Computer, engineering, and science occupations:!!Computer and mathematical occupations'),
('B24022_045E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Computer, engineering, and science occupations:!!Architecture and engineering occupations'),
('B24022_046E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Computer, engineering, and science occupations:!!Life, physical, and social science occupations'),
('B24022_047E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Education, legal, community service, arts, and media occupations:'),
('B24022_048E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Education, legal, community service, arts, and media occupations:!!Community and social service occupations'),
('B24022_049E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Education, legal, community service, arts, and media occupations:!!Legal occupations'),
('B24022_050E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Education, legal, community service, arts, and media occupations:!!Educational instruction, and library occupations'),
('B24022_051E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Education, legal, community service, arts, and media occupations:!!Arts, design, entertainment, sports, and media occupations'),
('B24022_052E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Healthcare practitioners and technical occupations:'),
('B24022_053E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Healthcare practitioners and technical occupations:!!Health diagnosing and treating practitioners and other technical occupations'),
('B24022_054E',
'SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Management, business, science, and arts occupations:!!Healthcare practitioners and technical occupations:!!Health technologists and technicians'),
('B24114_028E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Natural sciences managers'),
('B24114_081E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other mathematical science occupations'),
('B24114_125E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Agricultural and food science technicians'),
('B24114_128E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Environmental science and geoscience technicians'),
('B24114_130E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Social science research assistants'),
('B24114_131E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other life, physical, and social science technicians'),
('B24115_028E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Natural sciences managers'),
('B24115_081E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other mathematical science occupations'),
('B24115_125E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Agricultural and food science technicians'),
('B24115_128E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Environmental science and geoscience technicians'),
('B24115_130E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Social science research assistants'),
('B24115_131E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other life, physical, and social science technicians'),
('B24116_028E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Natural sciences managers'),
('B24116_081E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other mathematical science occupations'),
('B24116_125E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Agricultural and food science technicians'),
('B24116_128E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Environmental science and geoscience technicians'),
('B24116_130E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Social science research assistants'),
('B24116_131E',
'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other life, physical, and social science technicians'),
('B24121_028E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Natural sciences managers'),
('B24121_081E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other mathematical science occupations'),
('B24121_125E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Agricultural and food science technicians'),
('B24121_128E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Environmental science and geoscience technicians'),
('B24121_130E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Social science research assistants'),
('B24121_131E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other life, physical, and social science technicians'),
('B24122_028E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Natural sciences managers'),
('B24122_081E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other mathematical science occupations'),
('B24122_125E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Agricultural and food science technicians'),
('B24122_128E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Environmental science and geoscience technicians'),
('B24122_130E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Social science research assistants'),
('B24122_131E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other life, physical, and social science technicians'),
('B24123_028E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Natural sciences managers'),
('B24123_081E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other mathematical science occupations'),
('B24123_125E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Agricultural and food science technicians'),
('B24123_128E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Environmental science and geoscience technicians'),
('B24123_130E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Social science research assistants'),
('B24123_131E',
'DETAILED OCCUPATION BY MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other life, physical, and social science technicians'),
('B24124_028E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Natural sciences managers'),
('B24124_081E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other mathematical science occupations'),
('B24124_125E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Agricultural and food science technicians'),
('B24124_128E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Environmental science and geoscience technicians'),
('B24124_130E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Social science research assistants'),
('B24124_131E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other life, physical, and social science technicians'),
('B24125_028E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Natural sciences managers'),
('B24125_081E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other mathematical science occupations'),
('B24125_125E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Agricultural and food science technicians'),
('B24125_128E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Environmental science and geoscience technicians'),
('B24125_130E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Social science research assistants'),
('B24125_131E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED MALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other life, physical, and social science technicians'),
('B24126_028E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Natural sciences managers'),
('B24126_081E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other mathematical science occupations'),
('B24126_125E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Agricultural and food science technicians'),
('B24126_128E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Environmental science and geoscience technicians'),
('B24126_130E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Social science research assistants'),
('B24126_131E',
'DETAILED OCCUPATION FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED FEMALE POPULATION 16 YEARS AND OVER',
'Estimate!!Total:!!Other life, physical, and social science technicians'),
('B26115_002E',
'GROUP QUARTERS TYPE (3 TYPES) BY OCCUPATION',
'Estimate!!Total:!!Management, business, science, and arts occupations'),
('B26115_008E',
'GROUP QUARTERS TYPE (3 TYPES) BY OCCUPATION',
'Estimate!!Total:!!Noninstitutionalized group quarters population:!!Management, business, science, and arts occupations'),
('B26115_014E',
'GROUP QUARTERS TYPE (3 TYPES) BY OCCUPATION',
'Estimate!!Total:!!Noninstitutionalized group quarters population:!!College/university student housing:!!Management, business, science, and arts occupations'),
('B26215_002E',
'GROUP QUARTERS TYPE (5 TYPES) BY OCCUPATION',
'Estimate!!Total:!!Management, business, science, and arts occupations'),
('B26215_008E',
'GROUP QUARTERS TYPE (5 TYPES) BY OCCUPATION',
'Estimate!!Total:!!Noninstitutionalized group quarters population:!!Management, business, science, and arts occupations'),
('B26215_014E',
'GROUP QUARTERS TYPE (5 TYPES) BY OCCUPATION',
'Estimate!!Total:!!Noninstitutionalized group quarters population:!!College/university student housing:!!Management, business, science, and arts occupations'),
('B26215_020E',
'GROUP QUARTERS TYPE (5 TYPES) BY OCCUPATION',
'Estimate!!Total:!!Noninstitutionalized group quarters population:!!Military quarters/military ships:!!Management, business, science, and arts occupations'),
('C15010A_002E',
"FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR THE POPULATION 25 YEARS AND OVER (WHITE ALONE)",
'Estimate!!Total:!!Science and Engineering'),
('C15010A_003E',
"FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR THE POPULATION 25 YEARS AND OVER (WHITE ALONE)",
'Estimate!!Total:!!Science and Engineering Related Fields'),
('C15010B_002E',
"FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR THE POPULATION 25 YEARS AND OVER (BLACK OR AFRICAN AMERICAN ALONE)",
'Estimate!!Total:!!Science and Engineering'),
('C15010B_003E',
"FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR THE POPULATION 25 YEARS AND OVER (BLACK OR AFRICAN AMERICAN ALONE)",
'Estimate!!Total:!!Science and Engineering Related Fields'),
('C15010C_002E',
"FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR THE POPULATION 25 YEARS AND OVER (AMERICAN INDIAN AND ALASKA NATIVE ALONE)",
'Estimate!!Total:!!Science and Engineering'),
('C15010C_003E',
"FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR THE POPULATION 25 YEARS AND OVER (AMERICAN INDIAN AND ALASKA NATIVE ALONE)",
'Estimate!!Total:!!Science and Engineering Related Fields'),
('C15010D_002E',
"FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR THE POPULATION 25 YEARS AND OVER (ASIAN ALONE)",
'Estimate!!Total:!!Science and Engineering'),
('C15010D_003E',
"FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR THE POPULATION 25 YEARS AND OVER (ASIAN ALONE)",
'Estimate!!Total:!!Science and Engineering Related Fields'),
('C15010E_002E',
"FIELD OF BACHELOR'S DEGREE FOR FIRST MAJOR THE POPULATION 25 YEARS AND OVER (NATIVE HAWAIIAN AND OTHER PACIFIC ISLANDER ALONE)",
'Estimate!!Total:!!Science and Engineering')]
The result labeled as 'B24114_028E' contains 'DETAILED OCCUPATION FOR THE CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER' which seems like it may be relevant, so I'll save that value for use later.
Then, I'll make a query for education on the year 2019 using the AC5 database. Once again, we'll have to sift through the results to see if we can find something of interest/relevancy to us.
#Show first 10 results of search
censusdata.search('acs5', 2019, 'concept', 'education')[300:500]
[('B15001_053E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!25 to 34 years:!!Less than 9th grade'),
('B15001_054E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!25 to 34 years:!!9th to 12th grade, no diploma'),
('B15001_055E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!25 to 34 years:!!High school graduate (includes equivalency)'),
('B15001_056E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!25 to 34 years:!!Some college, no degree'),
('B15001_057E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
"Estimate!!Total:!!Female:!!25 to 34 years:!!Associate's degree"),
('B15001_058E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
"Estimate!!Total:!!Female:!!25 to 34 years:!!Bachelor's degree"),
('B15001_059E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!25 to 34 years:!!Graduate or professional degree'),
('B15001_060E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!35 to 44 years:'),
('B15001_061E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!35 to 44 years:!!Less than 9th grade'),
('B15001_062E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!35 to 44 years:!!9th to 12th grade, no diploma'),
('B15001_063E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!35 to 44 years:!!High school graduate (includes equivalency)'),
('B15001_064E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!35 to 44 years:!!Some college, no degree'),
('B15001_065E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
"Estimate!!Total:!!Female:!!35 to 44 years:!!Associate's degree"),
('B15001_066E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
"Estimate!!Total:!!Female:!!35 to 44 years:!!Bachelor's degree"),
('B15001_067E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!35 to 44 years:!!Graduate or professional degree'),
('B15001_068E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!45 to 64 years:'),
('B15001_069E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!45 to 64 years:!!Less than 9th grade'),
('B15001_070E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!45 to 64 years:!!9th to 12th grade, no diploma'),
('B15001_071E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!45 to 64 years:!!High school graduate (includes equivalency)'),
('B15001_072E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!45 to 64 years:!!Some college, no degree'),
('B15001_073E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
"Estimate!!Total:!!Female:!!45 to 64 years:!!Associate's degree"),
('B15001_074E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
"Estimate!!Total:!!Female:!!45 to 64 years:!!Bachelor's degree"),
('B15001_075E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!45 to 64 years:!!Graduate or professional degree'),
('B15001_076E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!65 years and over:'),
('B15001_077E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!65 years and over:!!Less than 9th grade'),
('B15001_078E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!65 years and over:!!9th to 12th grade, no diploma'),
('B15001_079E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!65 years and over:!!High school graduate (includes equivalency)'),
('B15001_080E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!65 years and over:!!Some college, no degree'),
('B15001_081E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
"Estimate!!Total:!!Female:!!65 years and over:!!Associate's degree"),
('B15001_082E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
"Estimate!!Total:!!Female:!!65 years and over:!!Bachelor's degree"),
('B15001_083E',
'SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER',
'Estimate!!Total:!!Female:!!65 years and over:!!Graduate or professional degree'),
('B15002_001E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:'),
('B15002_002E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:'),
('B15002_003E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!No schooling completed'),
('B15002_004E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Nursery to 4th grade'),
('B15002_005E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!5th and 6th grade'),
('B15002_006E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!7th and 8th grade'),
('B15002_007E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!9th grade'),
('B15002_008E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!10th grade'),
('B15002_009E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!11th grade'),
('B15002_010E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!12th grade, no diploma'),
('B15002_011E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!High school graduate (includes equivalency)'),
('B15002_012E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Some college, less than 1 year'),
('B15002_013E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Some college, 1 or more years, no degree'),
('B15002_014E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Male:!!Associate's degree"),
('B15002_015E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Male:!!Bachelor's degree"),
('B15002_016E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Male:!!Master's degree"),
('B15002_017E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Professional school degree'),
('B15002_018E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Male:!!Doctorate degree'),
('B15002_019E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:'),
('B15002_020E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!No schooling completed'),
('B15002_021E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Nursery to 4th grade'),
('B15002_022E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!5th and 6th grade'),
('B15002_023E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!7th and 8th grade'),
('B15002_024E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!9th grade'),
('B15002_025E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!10th grade'),
('B15002_026E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!11th grade'),
('B15002_027E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!12th grade, no diploma'),
('B15002_028E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!High school graduate (includes equivalency)'),
('B15002_029E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Some college, less than 1 year'),
('B15002_030E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Some college, 1 or more years, no degree'),
('B15002_031E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Female:!!Associate's degree"),
('B15002_032E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Female:!!Bachelor's degree"),
('B15002_033E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Female:!!Master's degree"),
('B15002_034E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Professional school degree'),
('B15002_035E',
'SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Female:!!Doctorate degree'),
('B15003_001E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:'),
('B15003_002E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!No schooling completed'),
('B15003_003E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Nursery school'),
('B15003_004E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Kindergarten'),
('B15003_005E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!1st grade'),
('B15003_006E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!2nd grade'),
('B15003_007E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!3rd grade'),
('B15003_008E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!4th grade'),
('B15003_009E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!5th grade'),
('B15003_010E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!6th grade'),
('B15003_011E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!7th grade'),
('B15003_012E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!8th grade'),
('B15003_013E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!9th grade'),
('B15003_014E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!10th grade'),
('B15003_015E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!11th grade'),
('B15003_016E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!12th grade, no diploma'),
('B15003_017E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Regular high school diploma'),
('B15003_018E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!GED or alternative credential'),
('B15003_019E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Some college, less than 1 year'),
('B15003_020E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Some college, 1 or more years, no degree'),
('B15003_021E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Associate's degree"),
('B15003_022E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree"),
('B15003_023E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Master's degree"),
('B15003_024E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Professional school degree'),
('B15003_025E',
'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Doctorate degree'),
('B16010_001E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:'),
('B16010_002E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:'),
('B16010_003E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!In labor force:'),
('B16010_004E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!In labor force:!!Speak only English'),
('B16010_005E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!In labor force:!!Speak Spanish'),
('B16010_006E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!In labor force:!!Speak other Indo-European languages'),
('B16010_007E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!In labor force:!!Speak Asian and Pacific Island languages'),
('B16010_008E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!In labor force:!!Speak other languages'),
('B16010_009E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!Not in labor force:'),
('B16010_010E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!Not in labor force:!!Speak only English'),
('B16010_011E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!Not in labor force:!!Speak Spanish'),
('B16010_012E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!Not in labor force:!!Speak other Indo-European languages'),
('B16010_013E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!Not in labor force:!!Speak Asian and Pacific Island languages'),
('B16010_014E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!Less than high school graduate:!!Not in labor force:!!Speak other languages'),
('B16010_015E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):'),
('B16010_016E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!In labor force:'),
('B16010_017E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!In labor force:!!Speak only English'),
('B16010_018E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!In labor force:!!Speak Spanish'),
('B16010_019E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!In labor force:!!Speak other Indo-European languages'),
('B16010_020E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!In labor force:!!Speak Asian and Pacific Island languages'),
('B16010_021E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!In labor force:!!Speak other languages'),
('B16010_022E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!Not in labor force:'),
('B16010_023E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!Not in labor force:!!Speak only English'),
('B16010_024E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!Not in labor force:!!Speak Spanish'),
('B16010_025E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!Not in labor force:!!Speak other Indo-European languages'),
('B16010_026E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!Not in labor force:!!Speak Asian and Pacific Island languages'),
('B16010_027E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
'Estimate!!Total:!!High school graduate (includes equivalency):!!Not in labor force:!!Speak other languages'),
('B16010_028E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:"),
('B16010_029E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!In labor force:"),
('B16010_030E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!In labor force:!!Speak only English"),
('B16010_031E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!In labor force:!!Speak Spanish"),
('B16010_032E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!In labor force:!!Speak other Indo-European languages"),
('B16010_033E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!In labor force:!!Speak Asian and Pacific Island languages"),
('B16010_034E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!In labor force:!!Speak other languages"),
('B16010_035E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!Not in labor force:"),
('B16010_036E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!Not in labor force:!!Speak only English"),
('B16010_037E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!Not in labor force:!!Speak Spanish"),
('B16010_038E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!Not in labor force:!!Speak other Indo-European languages"),
('B16010_039E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!Not in labor force:!!Speak Asian and Pacific Island languages"),
('B16010_040E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Some college or associate's degree:!!Not in labor force:!!Speak other languages"),
('B16010_041E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:"),
('B16010_042E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!In labor force:"),
('B16010_043E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!In labor force:!!Speak only English"),
('B16010_044E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!In labor force:!!Speak Spanish"),
('B16010_045E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!In labor force:!!Speak other Indo-European languages"),
('B16010_046E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!In labor force:!!Speak Asian and Pacific Island languages"),
('B16010_047E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!In labor force:!!Speak other languages"),
('B16010_048E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!Not in labor force:"),
('B16010_049E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!Not in labor force:!!Speak only English"),
('B16010_050E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!Not in labor force:!!Speak Spanish"),
('B16010_051E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!Not in labor force:!!Speak other Indo-European languages"),
('B16010_052E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!Not in labor force:!!Speak Asian and Pacific Island languages"),
('B16010_053E',
'EDUCATIONAL ATTAINMENT AND EMPLOYMENT STATUS BY LANGUAGE SPOKEN AT HOME FOR THE POPULATION 25 YEARS AND OVER',
"Estimate!!Total:!!Bachelor's degree or higher:!!Not in labor force:!!Speak other languages"),
('B17003_001E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:'),
('B17003_002E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months below poverty level:'),
('B17003_003E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Male:'),
('B17003_004E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Male:!!Less than high school graduate'),
('B17003_005E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Male:!!High school graduate (includes equivalency)'),
('B17003_006E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
"Estimate!!Total:!!Income in the past 12 months below poverty level:!!Male:!!Some college, associate's degree"),
('B17003_007E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
"Estimate!!Total:!!Income in the past 12 months below poverty level:!!Male:!!Bachelor's degree or higher"),
('B17003_008E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Female:'),
('B17003_009E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Female:!!Less than high school graduate'),
('B17003_010E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Female:!!High school graduate (includes equivalency)'),
('B17003_011E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
"Estimate!!Total:!!Income in the past 12 months below poverty level:!!Female:!!Some college, associate's degree"),
('B17003_012E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
"Estimate!!Total:!!Income in the past 12 months below poverty level:!!Female:!!Bachelor's degree or higher"),
('B17003_013E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:'),
('B17003_014E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Male:'),
('B17003_015E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Male:!!Less than high school graduate'),
('B17003_016E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Male:!!High school graduate (includes equivalency)'),
('B17003_017E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
"Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Male:!!Some college, associate's degree"),
('B17003_018E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
"Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Male:!!Bachelor's degree or higher"),
('B17003_019E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Female:'),
('B17003_020E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Female:!!Less than high school graduate'),
('B17003_021E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Female:!!High school graduate (includes equivalency)'),
('B17003_022E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
"Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Female:!!Some college, associate's degree"),
('B17003_023E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF INDIVIDUALS BY SEX BY EDUCATIONAL ATTAINMENT',
"Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Female:!!Bachelor's degree or higher"),
('B17018_001E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:'),
('B17018_002E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:'),
('B17018_003E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Married-couple family:'),
('B17018_004E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Married-couple family:!!Less than high school graduate'),
('B17018_005E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Married-couple family:!!High school graduate (includes equivalency)'),
('B17018_006E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
"Estimate!!Total:!!Income in the past 12 months below poverty level:!!Married-couple family:!!Some college, associate's degree"),
('B17018_007E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
"Estimate!!Total:!!Income in the past 12 months below poverty level:!!Married-couple family:!!Bachelor's degree or higher"),
('B17018_008E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:'),
('B17018_009E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:!!Male householder, no spouse present:'),
('B17018_010E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:!!Male householder, no spouse present:!!Less than high school graduate'),
('B17018_011E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:!!Male householder, no spouse present:!!High school graduate (includes equivalency)'),
('B17018_012E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
"Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:!!Male householder, no spouse present:!!Some college, associate's degree"),
('B17018_013E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
"Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:!!Male householder, no spouse present:!!Bachelor's degree or higher"),
('B17018_014E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:!!Female householder, no spouse present:'),
('B17018_015E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:!!Female householder, no spouse present:!!Less than high school graduate'),
('B17018_016E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:!!Female householder, no spouse present:!!High school graduate (includes equivalency)'),
('B17018_017E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
"Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:!!Female householder, no spouse present:!!Some college, associate's degree"),
('B17018_018E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
"Estimate!!Total:!!Income in the past 12 months below poverty level:!!Other families:!!Female householder, no spouse present:!!Bachelor's degree or higher"),
('B17018_019E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:'),
('B17018_020E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Married-couple family:'),
('B17018_021E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Married-couple family:!!Less than high school graduate'),
('B17018_022E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Married-couple family:!!High school graduate (includes equivalency)'),
('B17018_023E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
"Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Married-couple family:!!Some college, associate's degree"),
('B17018_024E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
"Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Married-couple family:!!Bachelor's degree or higher"),
('B17018_025E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Other families:'),
('B17018_026E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Other families:!!Male householder, no spouse present:'),
('B17018_027E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Other families:!!Male householder, no spouse present:!!Less than high school graduate'),
('B17018_028E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Other families:!!Male householder, no spouse present:!!High school graduate (includes equivalency)'),
('B17018_029E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
"Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Other families:!!Male householder, no spouse present:!!Some college, associate's degree"),
('B17018_030E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
"Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Other families:!!Male householder, no spouse present:!!Bachelor's degree or higher"),
('B17018_031E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Other families:!!Female householder, no spouse present:'),
('B17018_032E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Other families:!!Female householder, no spouse present:!!Less than high school graduate'),
('B17018_033E',
'POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER',
'Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!Other families:!!Female householder, no spouse present:!!High school graduate (includes equivalency)')]
The entry labeled under 'B15003_025E' has information on the 'EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER'. This may have some stuff that is of use to us.
Next, let's look at what data each of those reports contain.
censusdata.printtable(censusdata.censustable('acs5', 2019, 'B24114'))
Variable | Table | Label | Type ------------------------------------------------------------------------------------------------------------------- B24114_001E | DETAILED OCCUPATION FOR THE CI | !! Estimate Total: | int B24114_002E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Chief executives | int B24114_003E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: General and operations managers | int B24114_004E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Legislators | int B24114_005E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Advertising and promotions manager | int B24114_006E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Marketing managers | int B24114_007E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Sales managers | int B24114_008E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Public relations and fundraising m | int B24114_009E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Administrative services managers | int B24114_010E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Facilities managers | int B24114_011E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Computer and information systems m | int B24114_012E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Financial managers | int B24114_013E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Compensation and benefits managers | int B24114_014E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Human resources managers | int B24114_015E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Training and development managers | int B24114_016E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Industrial production managers | int B24114_017E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Purchasing managers | int B24114_018E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Transportation, storage, and distr | int B24114_019E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Farmers, ranchers, and other agric | int B24114_020E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Construction managers | int B24114_021E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Education and childcare administra | int B24114_022E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Architectural and engineering mana | int B24114_023E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Food service managers | int B24114_024E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Funeral home managers | int B24114_025E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Entertainment and recreation manag | int B24114_026E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Lodging managers | int B24114_027E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Medical and health services manage | int B24114_028E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Natural sciences managers | int B24114_029E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Postmasters and mail superintenden | int B24114_030E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Property, real estate, and communi | int B24114_031E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Social and community service manag | int B24114_032E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Emergency management directors | int B24114_033E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Personal service managers, all oth | int B24114_034E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Managers, all other | int B24114_035E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Agents and business managers of ar | int B24114_036E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Buyers and purchasing agents, farm | int B24114_037E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Wholesale and retail buyers, excep | int B24114_038E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Purchasing agents, except wholesal | int B24114_039E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Claims adjusters, appraisers, exam | int B24114_040E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Compliance officers | int B24114_041E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Cost estimators | int B24114_042E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Human resources workers | int B24114_043E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Compensation, benefits, and job an | int B24114_044E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Training and development specialis | int B24114_045E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Logisticians | int B24114_046E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Project management specialists | int B24114_047E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Management analysts | int B24114_048E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Meeting, convention, and event pla | int B24114_049E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Fundraisers | int B24114_050E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Market research analysts and marke | int B24114_051E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Business operations specialists, a | int B24114_052E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Accountants and auditors | int B24114_053E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Property appraisers and assessors | int B24114_054E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Budget analysts | int B24114_055E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Credit analysts | int B24114_056E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Financial and investment analysts | int B24114_057E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Personal financial advisors | int B24114_058E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Insurance underwriters | int B24114_059E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Financial examiners | int B24114_060E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Credit counselors and loan officer | int B24114_061E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Tax examiners and collectors, and | int B24114_062E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Tax preparers | int B24114_063E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other financial specialists | int B24114_064E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Computer and information research | int B24114_065E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Computer systems analysts | int B24114_066E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Information security analysts | int B24114_067E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Computer programmers | int B24114_068E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Software developers | int B24114_069E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Software quality assurance analyst | int B24114_070E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Web developers | int B24114_071E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Web and digital interface designer | int B24114_072E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Computer support specialists | int B24114_073E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Database administrators and archit | int B24114_074E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Network and computer systems admin | int B24114_075E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Computer network architects | int B24114_076E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Computer occupations, all other | int B24114_077E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Actuaries | int B24114_078E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Mathematicians | int B24114_079E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Operations research analysts | int B24114_080E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Statisticians | int B24114_081E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other mathematical science occupat | int B24114_082E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Architects, except landscape and n | int B24114_083E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Landscape architects | int B24114_084E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Surveyors, cartographers, and phot | int B24114_085E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Aerospace engineers | int B24114_086E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Agricultural engineers | int B24114_087E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Bioengineers and biomedical engine | int B24114_088E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Chemical engineers | int B24114_089E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Civil engineers | int B24114_090E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Computer hardware engineers | int B24114_091E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Electrical and electronics enginee | int B24114_092E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Environmental engineers | int B24114_093E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Industrial engineers, including he | int B24114_094E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Marine engineers and naval archite | int B24114_095E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Materials engineers | int B24114_096E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Mechanical engineers | int B24114_097E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Mining and geological engineers, i | int B24114_098E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Nuclear engineers | int B24114_099E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Petroleum engineers | int B24114_100E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Engineers, all other | int B24114_101E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Architectural and civil drafters | int B24114_102E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other drafters | int B24114_103E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Electrical and electronic engineer | int B24114_104E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other engineering technologists an | int B24114_105E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Surveying and mapping technicians | int B24114_106E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Agricultural and food scientists | int B24114_107E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Biological scientists | int B24114_108E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Conservation scientists and forest | int B24114_109E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Medical scientists | int B24114_110E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Life scientists, all other | int B24114_111E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Astronomers and physicists | int B24114_112E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Atmospheric and space scientists | int B24114_113E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Chemists and materials scientists | int B24114_114E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Environmental scientists and speci | int B24114_115E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Geoscientists and hydrologists, ex | int B24114_116E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Physical scientists, all other | int B24114_117E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Economists | int B24114_118E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Survey researchers | int B24114_119E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Clinical and counseling psychologi | int B24114_120E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: School psychologists | int B24114_121E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other psychologists | int B24114_122E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Sociologists | int B24114_123E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Urban and regional planners | int B24114_124E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Miscellaneous social scientists an | int B24114_125E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Agricultural and food science tech | int B24114_126E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Biological technicians | int B24114_127E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Chemical technicians | int B24114_128E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Environmental science and geoscien | int B24114_129E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Nuclear technicians | int B24114_130E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Social science research assistants | int B24114_131E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other life, physical, and social s | int B24114_132E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Occupational health and safety spe | int B24114_133E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Substance abuse and behavioral dis | int B24114_134E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Educational, guidance, and career | int B24114_135E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Marriage and family therapists | int B24114_136E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Mental health counselors | int B24114_137E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Rehabilitation counselors | int B24114_138E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Counselors, all other | int B24114_139E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Child, family, and school social w | int B24114_140E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Healthcare social workers | int B24114_141E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Mental health and substance abuse | int B24114_142E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Social workers, all other | int B24114_143E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Probation officers and correctiona | int B24114_144E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Social and human service assistant | int B24114_145E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other community and social service | int B24114_146E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Clergy | int B24114_147E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Directors, religious activities an | int B24114_148E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Religious workers, all other | int B24114_149E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Lawyers | int B24114_150E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Judicial law clerks | int B24114_151E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Judges, magistrates, and other jud | int B24114_152E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Paralegals and legal assistants | int B24114_153E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Title examiners, abstractors, and | int B24114_154E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Legal support workers, all other | int B24114_155E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Postsecondary teachers | int B24114_156E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Preschool and kindergarten teacher | int B24114_157E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Elementary and middle school teach | int B24114_158E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Secondary school teachers | int B24114_159E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Special education teachers | int B24114_160E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Tutors | int B24114_161E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other teachers and instructors | int B24114_162E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Archivists, curators, and museum t | int B24114_163E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Librarians and media collections s | int B24114_164E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Library technicians | int B24114_165E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Teaching assistants | int B24114_166E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other educational instruction and | int B24114_167E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Artists and related workers | int B24114_168E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Commercial and industrial designer | int B24114_169E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Fashion designers | int B24114_170E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Floral designers | int B24114_171E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Graphic designers | int B24114_172E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Interior designers | int B24114_173E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Merchandise displayers and window | int B24114_174E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other designers | int B24114_175E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Actors | int B24114_176E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Producers and directors | int B24114_177E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Athletes and sports competitors | int B24114_178E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Coaches and scouts | int B24114_179E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Umpires, referees, and other sport | int B24114_180E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Dancers and choreographers | int B24114_181E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Music directors and composers | int B24114_182E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Musicians and singers | int B24114_183E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Disc jockeys, except radio | int B24114_184E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Entertainers and performers, sport | int B24114_185E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Broadcast announcers and radio dis | int B24114_186E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: News analysts, reporters, and jour | int B24114_187E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Public relations specialists | int B24114_188E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Editors | int B24114_189E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Technical writers | int B24114_190E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Writers and authors | int B24114_191E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Interpreters and translators | int B24114_192E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Court reporters and simultaneous c | int B24114_193E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Media and communication workers, a | int B24114_194E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Broadcast, sound, and lighting tec | int B24114_195E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Photographers | int B24114_196E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Television, video, and film camera | int B24114_197E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Media and communication equipment | int B24114_198E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Chiropractors | int B24114_199E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Dentists | int B24114_200E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Dietitians and nutritionists | int B24114_201E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Optometrists | int B24114_202E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Pharmacists | int B24114_203E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Emergency medicine physicians | int B24114_204E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Radiologists | int B24114_205E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other physicians | int B24114_206E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Surgeons | int B24114_207E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Physician assistants | int B24114_208E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Podiatrists | int B24114_209E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Audiologists | int B24114_210E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Occupational therapists | int B24114_211E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Physical therapists | int B24114_212E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Radiation therapists | int B24114_213E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Recreational therapists | int B24114_214E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Respiratory therapists | int B24114_215E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Speech-language pathologists | int B24114_216E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Exercise physiologists | int B24114_217E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Therapists, all other | int B24114_218E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Veterinarians | int B24114_219E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Registered nurses | int B24114_220E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Nurse anesthetists | int B24114_221E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Nurse midwives | int B24114_222E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Nurse practitioners | int B24114_223E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Acupuncturists | int B24114_224E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Healthcare diagnosing or treating | int B24114_225E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Clinical laboratory technologists | int B24114_226E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Dental hygienists | int B24114_227E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Cardiovascular technologists and t | int B24114_228E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Diagnostic medical sonographers | int B24114_229E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Radiologic technologists and techn | int B24114_230E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Magnetic resonance imaging technol | int B24114_231E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Nuclear medicine technologists and | int B24114_232E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Emergency medical technicians | int B24114_233E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Paramedics | int B24114_234E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Pharmacy technicians | int B24114_235E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Psychiatric technicians | int B24114_236E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Surgical technologists | int B24114_237E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Veterinary technologists and techn | int B24114_238E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Dietetic technicians and ophthalmi | int B24114_239E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Licensed practical and licensed vo | int B24114_240E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Medical records specialists | int B24114_241E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Opticians, dispensing | int B24114_242E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Miscellaneous health technologists | int B24114_243E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other healthcare practitioners and | int B24114_244E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Home health aides | int B24114_245E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Personal care aides | int B24114_246E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Nursing assistants | int B24114_247E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Orderlies and psychiatric aides | int B24114_248E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Occupational therapy assistants an | int B24114_249E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Physical therapist assistants and | int B24114_250E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Massage therapists | int B24114_251E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Dental assistants | int B24114_252E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Medical assistants | int B24114_253E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Medical transcriptionists | int B24114_254E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Pharmacy aides | int B24114_255E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Veterinary assistants and laborato | int B24114_256E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Phlebotomists | int B24114_257E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other healthcare support workers | int B24114_258E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of correcti | int B24114_259E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of police a | int B24114_260E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of firefigh | int B24114_261E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of security | int B24114_262E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of protecti | int B24114_263E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Firefighters | int B24114_264E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Fire inspectors | int B24114_265E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Bailiffs | int B24114_266E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Correctional officers and jailers | int B24114_267E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Detectives and criminal investigat | int B24114_268E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Fish and game wardens | int B24114_269E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Parking enforcement workers | int B24114_270E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Police officers | int B24114_271E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Animal control workers | int B24114_272E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Private detectives and investigato | int B24114_273E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Security guards and gambling surve | int B24114_274E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Crossing guards and flaggers | int B24114_275E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Transportation security screeners | int B24114_276E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: School bus monitors | int B24114_277E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other protective service workers | int B24114_278E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Chefs and head cooks | int B24114_279E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of food pre | int B24114_280E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Cooks | int B24114_281E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Food preparation workers | int B24114_282E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Bartenders | int B24114_283E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Fast food and counter workers | int B24114_284E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Waiters and waitresses | int B24114_285E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Food servers, nonrestaurant | int B24114_286E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Dining room and cafeteria attendan | int B24114_287E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Dishwashers | int B24114_288E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Hosts and hostesses, restaurant, l | int B24114_289E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Food preparation and serving relat | int B24114_290E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of housekee | int B24114_291E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of landscap | int B24114_292E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Janitors and building cleaners | int B24114_293E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Maids and housekeeping cleaners | int B24114_294E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Pest control workers | int B24114_295E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Landscaping and groundskeeping wor | int B24114_296E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Tree trimmers and pruners | int B24114_297E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other grounds maintenance workers | int B24114_298E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Supervisors of personal care and s | int B24114_299E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Animal trainers | int B24114_300E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Animal caretakers | int B24114_301E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Gambling services workers | int B24114_302E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Ushers, lobby attendants, and tick | int B24114_303E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other entertainment attendants and | int B24114_304E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Embalmers, crematory operators and | int B24114_305E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Morticians, undertakers, and funer | int B24114_306E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Barbers | int B24114_307E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Hairdressers, hairstylists, and co | int B24114_308E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Manicurists and pedicurists | int B24114_309E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Skincare specialists | int B24114_310E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other personal appearance workers | int B24114_311E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Baggage porters, bellhops, and con | int B24114_312E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Tour and travel guides | int B24114_313E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Childcare workers | int B24114_314E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Exercise trainers and group fitnes | int B24114_315E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Recreation workers | int B24114_316E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Residential advisors | int B24114_317E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Personal care and service workers, | int B24114_318E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of retail s | int B24114_319E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of non-reta | int B24114_320E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Cashiers | int B24114_321E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Counter and rental clerks | int B24114_322E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Parts salespersons | int B24114_323E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Retail salespersons | int B24114_324E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Advertising sales agents | int B24114_325E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Insurance sales agents | int B24114_326E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Securities, commodities, and finan | int B24114_327E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Travel agents | int B24114_328E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Sales representatives of services, | int B24114_329E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Sales representatives, wholesale a | int B24114_330E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Models, demonstrators, and product | int B24114_331E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Real estate brokers and sales agen | int B24114_332E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Sales engineers | int B24114_333E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Telemarketers | int B24114_334E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Door-to-door sales workers, news a | int B24114_335E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Sales and related workers, all oth | int B24114_336E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of office a | int B24114_337E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Switchboard operators, including a | int B24114_338E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Telephone operators | int B24114_339E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Communications equipment operators | int B24114_340E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Bill and account collectors | int B24114_341E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Billing and posting clerks | int B24114_342E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Bookkeeping, accounting, and audit | int B24114_343E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Gambling cage workers | int B24114_344E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Payroll and timekeeping clerks | int B24114_345E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Procurement clerks | int B24114_346E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Tellers | int B24114_347E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Financial clerks, all other | int B24114_348E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Brokerage clerks | int B24114_349E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Correspondence clerks | int B24114_350E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Court, municipal, and license cler | int B24114_351E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Credit authorizers, checkers, and | int B24114_352E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Customer service representatives | int B24114_353E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Eligibility interviewers, governme | int B24114_354E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: File clerks | int B24114_355E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Hotel, motel, and resort desk cler | int B24114_356E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Interviewers, except eligibility a | int B24114_357E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Library assistants, clerical | int B24114_358E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Loan interviewers and clerks | int B24114_359E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: New accounts clerks | int B24114_360E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Order clerks | int B24114_361E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Human resources assistants, except | int B24114_362E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Receptionists and information cler | int B24114_363E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Reservation and transportation tic | int B24114_364E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Information and record clerks, all | int B24114_365E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Cargo and freight agents | int B24114_366E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Couriers and messengers | int B24114_367E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Public safety telecommunicators | int B24114_368E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Dispatchers, except police, fire, | int B24114_369E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Meter readers, utilities | int B24114_370E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Postal service clerks | int B24114_371E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Postal service mail carriers | int B24114_372E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Postal service mail sorters, proce | int B24114_373E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Production, planning, and expediti | int B24114_374E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Shipping, receiving, and inventory | int B24114_375E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Weighers, measurers, checkers, and | int B24114_376E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Executive secretaries and executiv | int B24114_377E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Legal secretaries and administrati | int B24114_378E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Medical secretaries and administra | int B24114_379E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Secretaries and administrative ass | int B24114_380E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Data entry keyers | int B24114_381E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Word processors and typists | int B24114_382E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Desktop publishers | int B24114_383E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Insurance claims and policy proces | int B24114_384E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Mail clerks and mail machine opera | int B24114_385E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Office clerks, general | int B24114_386E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Office machine operators, except c | int B24114_387E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Proofreaders and copy markers | int B24114_388E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Statistical assistants | int B24114_389E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Office and administrative support | int B24114_390E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of farming, | int B24114_391E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Agricultural inspectors | int B24114_392E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Animal breeders | int B24114_393E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Graders and sorters, agricultural | int B24114_394E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Miscellaneous agricultural workers | int B24114_395E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Fishing and hunting workers | int B24114_396E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Forest and conservation workers | int B24114_397E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Logging workers | int B24114_398E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of construc | int B24114_399E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Boilermakers | int B24114_400E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Brickmasons, blockmasons, and ston | int B24114_401E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Carpenters | int B24114_402E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Carpet, floor, and tile installers | int B24114_403E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Cement masons, concrete finishers, | int B24114_404E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Construction laborers | int B24114_405E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Construction equipment operators | int B24114_406E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Drywall installers, ceiling tile i | int B24114_407E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Electricians | int B24114_408E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Glaziers | int B24114_409E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Insulation workers | int B24114_410E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Painters and paperhangers | int B24114_411E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Pipelayers | int B24114_412E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Plumbers, pipefitters, and steamfi | int B24114_413E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Plasterers and stucco masons | int B24114_414E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Reinforcing iron and rebar workers | int B24114_415E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Roofers | int B24114_416E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Sheet metal workers | int B24114_417E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Structural iron and steel workers | int B24114_418E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Solar photovoltaic installers | int B24114_419E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Helpers, construction trades | int B24114_420E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Construction and building inspecto | int B24114_421E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Elevator and escalator installers | int B24114_422E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Fence erectors | int B24114_423E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Hazardous materials removal worker | int B24114_424E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Highway maintenance workers | int B24114_425E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Rail-track laying and maintenance | int B24114_426E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Septic tank servicers and sewer pi | int B24114_427E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Miscellaneous construction and rel | int B24114_428E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Derrick, rotary drill, and service | int B24114_429E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Excavating and loading machine and | int B24114_430E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Earth drillers, except oil and gas | int B24114_431E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Explosives workers, ordnance handl | int B24114_432E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Underground mining machine operato | int B24114_433E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Roustabouts, oil and gas | int B24114_434E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other extraction workers | int B24114_435E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of mechanic | int B24114_436E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Computer, automated teller, and of | int B24114_437E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Radio and telecommunications equip | int B24114_438E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Avionics technicians | int B24114_439E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Electric motor, power tool, and re | int B24114_440E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Electrical and electronics install | int B24114_441E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Electrical and electronics repaire | int B24114_442E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Electronic equipment installers an | int B24114_443E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Audiovisual equipment installers a | int B24114_444E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Security and fire alarm systems in | int B24114_445E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Aircraft mechanics and service tec | int B24114_446E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Automotive body and related repair | int B24114_447E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Automotive glass installers and re | int B24114_448E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Automotive service technicians and | int B24114_449E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Bus and truck mechanics and diesel | int B24114_450E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Heavy vehicle and mobile equipment | int B24114_451E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Small engine mechanics | int B24114_452E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Miscellaneous vehicle and mobile e | int B24114_453E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Control and valve installers and r | int B24114_454E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Heating, air conditioning, and ref | int B24114_455E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Home appliance repairers | int B24114_456E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Industrial and refractory machiner | int B24114_457E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Maintenance and repair workers, ge | int B24114_458E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Maintenance workers, machinery | int B24114_459E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Millwrights | int B24114_460E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Electrical power-line installers a | int B24114_461E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Telecommunications line installers | int B24114_462E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Precision instrument and equipment | int B24114_463E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Wind turbine service technicians | int B24114_464E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Coin, vending, and amusement machi | int B24114_465E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Commercial divers | int B24114_466E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Locksmiths and safe repairers | int B24114_467E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Manufactured building and mobile h | int B24114_468E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Riggers | int B24114_469E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Helpers--installation, maintenance | int B24114_470E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other installation, maintenance, a | int B24114_471E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: First-line supervisors of producti | int B24114_472E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Aircraft structure, surfaces, rigg | int B24114_473E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Electrical, electronics, and elect | int B24114_474E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Engine and other machine assembler | int B24114_475E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Structural metal fabricators and f | int B24114_476E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other assemblers and fabricators | int B24114_477E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Bakers | int B24114_478E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Butchers and other meat, poultry, | int B24114_479E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Food and tobacco roasting, baking, | int B24114_480E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Food batchmakers | int B24114_481E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Food cooking machine operators and | int B24114_482E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Food processing workers, all other | int B24114_483E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Computer numerically controlled to | int B24114_484E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Forming machine setters, operators | int B24114_485E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Cutting, punching, and press machi | int B24114_486E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Grinding, lapping, polishing, and | int B24114_487E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other machine tool setters, operat | int B24114_488E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Machinists | int B24114_489E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Metal furnace operators, tenders, | int B24114_490E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Model makers and patternmakers, me | int B24114_491E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Molders and molding machine setter | int B24114_492E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Tool and die makers | int B24114_493E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Welding, soldering, and brazing wo | int B24114_494E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other metal workers and plastic wo | int B24114_495E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Prepress technicians and workers | int B24114_496E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Printing press operators | int B24114_497E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Print binding and finishing worker | int B24114_498E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Laundry and dry-cleaning workers | int B24114_499E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Pressers, textile, garment, and re | int B24114_500E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Sewing machine operators | int B24114_501E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Shoe and leather workers | int B24114_502E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Tailors, dressmakers, and sewers | int B24114_503E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Textile machine setters, operators | int B24114_504E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Upholsterers | int B24114_505E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other textile, apparel, and furnis | int B24114_506E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Cabinetmakers and bench carpenters | int B24114_507E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Furniture finishers | int B24114_508E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Sawing machine setters, operators, | int B24114_509E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Woodworking machine setters, opera | int B24114_510E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other woodworkers | int B24114_511E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Power plant operators, distributor | int B24114_512E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Stationary engineers and boiler op | int B24114_513E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Water and wastewater treatment pla | int B24114_514E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Miscellaneous plant and system ope | int B24114_515E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Chemical processing machine setter | int B24114_516E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Crushing, grinding, polishing, mix | int B24114_517E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Cutting workers | int B24114_518E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Extruding, forming, pressing, and | int B24114_519E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Furnace, kiln, oven, drier, and ke | int B24114_520E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Inspectors, testers, sorters, samp | int B24114_521E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Jewelers and precious stone and me | int B24114_522E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Dental and ophthalmic laboratory t | int B24114_523E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Packaging and filling machine oper | int B24114_524E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Painting workers | int B24114_525E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Photographic process workers and p | int B24114_526E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Adhesive bonding machine operators | int B24114_527E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Etchers and engravers | int B24114_528E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Molders, shapers, and casters, exc | int B24114_529E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Paper goods machine setters, opera | int B24114_530E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Tire builders | int B24114_531E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Helpers--production workers | int B24114_532E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other production equipment operato | int B24114_533E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other production workers | int B24114_534E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Supervisors of transportation and | int B24114_535E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Aircraft pilots and flight enginee | int B24114_536E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Air traffic controllers and airfie | int B24114_537E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Flight attendants | int B24114_538E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Ambulance drivers and attendants, | int B24114_539E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Bus drivers, school | int B24114_540E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Bus drivers, transit and intercity | int B24114_541E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Driver/sales workers and truck dri | int B24114_542E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Shuttle drivers and chauffeurs | int B24114_543E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Taxi drivers | int B24114_544E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Motor vehicle operators, all other | int B24114_545E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Locomotive engineers and operators | int B24114_546E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Railroad conductors and yardmaster | int B24114_547E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other rail transportation workers | int B24114_548E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Sailors and marine oilers | int B24114_549E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Ship and boat captains and operato | int B24114_550E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Ship engineers | int B24114_551E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Parking attendants | int B24114_552E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Transportation service attendants | int B24114_553E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Transportation inspectors | int B24114_554E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Passenger attendants | int B24114_555E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other transportation workers | int B24114_556E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Crane and tower operators | int B24114_557E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Conveyor, dredge, and hoist and wi | int B24114_558E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Industrial truck and tractor opera | int B24114_559E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Cleaners of vehicles and equipment | int B24114_560E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Laborers and freight, stock, and m | int B24114_561E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Machine feeders and offbearers | int B24114_562E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Packers and packagers, hand | int B24114_563E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Stockers and order fillers | int B24114_564E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Pumping station operators | int B24114_565E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Refuse and recyclable material col | int B24114_566E | DETAILED OCCUPATION FOR THE CI | !! !! Estimate Total: Other material moving workers | int -------------------------------------------------------------------------------------------------------------------
censusdata.printtable(censusdata.censustable('acs5', 2019, 'B15003'))
Variable | Table | Label | Type ------------------------------------------------------------------------------------------------------------------- B15003_001E | EDUCATIONAL ATTAINMENT FOR THE | !! Estimate Total: | int B15003_002E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: No schooling completed | int B15003_003E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: Nursery school | int B15003_004E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: Kindergarten | int B15003_005E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 1st grade | int B15003_006E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 2nd grade | int B15003_007E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 3rd grade | int B15003_008E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 4th grade | int B15003_009E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 5th grade | int B15003_010E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 6th grade | int B15003_011E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 7th grade | int B15003_012E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 8th grade | int B15003_013E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 9th grade | int B15003_014E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 10th grade | int B15003_015E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 11th grade | int B15003_016E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: 12th grade, no diploma | int B15003_017E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: Regular high school diploma | int B15003_018E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: GED or alternative credential | int B15003_019E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: Some college, less than 1 year | int B15003_020E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: Some college, 1 or more years, no | int B15003_021E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: Associate's degree | int B15003_022E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: Bachelor's degree | int B15003_023E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: Master's degree | int B15003_024E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: Professional school degree | int B15003_025E | EDUCATIONAL ATTAINMENT FOR THE | !! !! Estimate Total: Doctorate degree | int -------------------------------------------------------------------------------------------------------------------
These two reports may have something of use. The first report contains information about a variety of occupations held by civilians while the second report has information about the education levels of civilians.
Now that we have the variables we want to explore (i.e. position held and education level) we can start getting more geographic specific info. To do that we use censusdata.censusgeo.censusgeo(geo, name='') which needs two inputs:
We'll also need the censusdata.download.geographies(within, src, year, key=None, endpt='') function to download that data. Let's find out what the code is for New Jersey by looking up the state data using the wildcard for names.
censusdata.geographies(censusdata.censusgeo([('state', '*')]), 'acs5', 2019)
{'Alabama': censusgeo((('state', '01'),)),
'Alaska': censusgeo((('state', '02'),)),
'Arizona': censusgeo((('state', '04'),)),
'Arkansas': censusgeo((('state', '05'),)),
'California': censusgeo((('state', '06'),)),
'Colorado': censusgeo((('state', '08'),)),
'Delaware': censusgeo((('state', '10'),)),
'District of Columbia': censusgeo((('state', '11'),)),
'Connecticut': censusgeo((('state', '09'),)),
'Florida': censusgeo((('state', '12'),)),
'Georgia': censusgeo((('state', '13'),)),
'Idaho': censusgeo((('state', '16'),)),
'Hawaii': censusgeo((('state', '15'),)),
'Illinois': censusgeo((('state', '17'),)),
'Indiana': censusgeo((('state', '18'),)),
'Iowa': censusgeo((('state', '19'),)),
'Kansas': censusgeo((('state', '20'),)),
'Kentucky': censusgeo((('state', '21'),)),
'Louisiana': censusgeo((('state', '22'),)),
'Maine': censusgeo((('state', '23'),)),
'Maryland': censusgeo((('state', '24'),)),
'Massachusetts': censusgeo((('state', '25'),)),
'Michigan': censusgeo((('state', '26'),)),
'Minnesota': censusgeo((('state', '27'),)),
'Mississippi': censusgeo((('state', '28'),)),
'Missouri': censusgeo((('state', '29'),)),
'Montana': censusgeo((('state', '30'),)),
'Nebraska': censusgeo((('state', '31'),)),
'Nevada': censusgeo((('state', '32'),)),
'New Hampshire': censusgeo((('state', '33'),)),
'New Jersey': censusgeo((('state', '34'),)),
'New Mexico': censusgeo((('state', '35'),)),
'New York': censusgeo((('state', '36'),)),
'North Carolina': censusgeo((('state', '37'),)),
'North Dakota': censusgeo((('state', '38'),)),
'Ohio': censusgeo((('state', '39'),)),
'Oklahoma': censusgeo((('state', '40'),)),
'Oregon': censusgeo((('state', '41'),)),
'Pennsylvania': censusgeo((('state', '42'),)),
'Rhode Island': censusgeo((('state', '44'),)),
'South Carolina': censusgeo((('state', '45'),)),
'South Dakota': censusgeo((('state', '46'),)),
'Tennessee': censusgeo((('state', '47'),)),
'Texas': censusgeo((('state', '48'),)),
'Vermont': censusgeo((('state', '50'),)),
'Utah': censusgeo((('state', '49'),)),
'Virginia': censusgeo((('state', '51'),)),
'Washington': censusgeo((('state', '53'),)),
'West Virginia': censusgeo((('state', '54'),)),
'Wisconsin': censusgeo((('state', '55'),)),
'Wyoming': censusgeo((('state', '56'),)),
'Puerto Rico': censusgeo((('state', '72'),))}
We find that entry for is 'New Jersey': censusgeo((('state', '34'),)), which means the code we need to enter on censusgeo is ('state', '34').
Next, let's find Hudson County. We'll use a similar process as before but include, 'county' as part of our search.
censusdata.geographies(censusdata.censusgeo([('state', '34'), ('county', '*')]), 'acs5', 2019)
{'Monmouth County, New Jersey': censusgeo((('state', '34'), ('county', '025'))),
'Sussex County, New Jersey': censusgeo((('state', '34'), ('county', '037'))),
'Essex County, New Jersey': censusgeo((('state', '34'), ('county', '013'))),
'Gloucester County, New Jersey': censusgeo((('state', '34'), ('county', '015'))),
'Passaic County, New Jersey': censusgeo((('state', '34'), ('county', '031'))),
'Bergen County, New Jersey': censusgeo((('state', '34'), ('county', '003'))),
'Warren County, New Jersey': censusgeo((('state', '34'), ('county', '041'))),
'Burlington County, New Jersey': censusgeo((('state', '34'), ('county', '005'))),
'Mercer County, New Jersey': censusgeo((('state', '34'), ('county', '021'))),
'Cumberland County, New Jersey': censusgeo((('state', '34'), ('county', '011'))),
'Somerset County, New Jersey': censusgeo((('state', '34'), ('county', '035'))),
'Ocean County, New Jersey': censusgeo((('state', '34'), ('county', '029'))),
'Atlantic County, New Jersey': censusgeo((('state', '34'), ('county', '001'))),
'Middlesex County, New Jersey': censusgeo((('state', '34'), ('county', '023'))),
'Salem County, New Jersey': censusgeo((('state', '34'), ('county', '033'))),
'Cape May County, New Jersey': censusgeo((('state', '34'), ('county', '009'))),
'Camden County, New Jersey': censusgeo((('state', '34'), ('county', '007'))),
'Union County, New Jersey': censusgeo((('state', '34'), ('county', '039'))),
'Hudson County, New Jersey': censusgeo((('state', '34'), ('county', '017'))),
'Hunterdon County, New Jersey': censusgeo((('state', '34'), ('county', '019'))),
'Morris County, New Jersey': censusgeo((('state', '34'), ('county', '027')))}
Nice! Here are all the counties of New Jersey and now we can get the information specific to Hudson County 'Hudson County, New Jersey': censusgeo((('state', '34'), ('county', '017')))' by adding ('county', '017') to our search.
Now that we have the state and county identifiers, we can extract the specific data blocks that we want from each dataset.
I'll extract the following 5 blocks from the B15003 report: B15003_017E, B15003_021E, B15003_022E, B15003_023E, and B15003_025E which correspond to education levels of regular high school diploma, associates degree, bachelors degree, masters degree and doctorate degree.
I'll extract the following 5 blocks from the B24114 report: B24114_017E, B24114_021E, B24114_022E, B24114_023E, and B24114_025E which correspond to employment positions of Chief executives , legislators, information security analysts, library technicians, and firefighters.
cookbg = censusdata.download('acs5', 2019,
censusdata.censusgeo([('state', '34'), ('county', '017'), ('block group', '*')]),
['B15003_017E', 'B15003_021E', 'B15003_022E', 'B15003_023E', 'B15003_025E',
'B24114_002E', 'B24114_004E', 'B24114_066E', 'B24114_164E', 'B24114_263E'])
cookbg.head()
| B15003_017E | B15003_021E | B15003_022E | B15003_023E | B15003_025E | B24114_002E | B24114_004E | B24114_066E | B24114_164E | B24114_263E | |
|---|---|---|---|---|---|---|---|---|---|---|
| Block Group 3, Census Tract 199, Hudson County, New Jersey: Summary level: 150, state:34> county:017> tract:019900> block group:3 | 179 | 33 | 95 | 76 | 15 | None | None | None | None | None |
| Block Group 2, Census Tract 198, Hudson County, New Jersey: Summary level: 150, state:34> county:017> tract:019800> block group:2 | 554 | 113 | 579 | 242 | 61 | None | None | None | None | None |
| Block Group 1, Census Tract 198, Hudson County, New Jersey: Summary level: 150, state:34> county:017> tract:019800> block group:1 | 461 | 126 | 846 | 538 | 0 | None | None | None | None | None |
| Block Group 1, Census Tract 158.02, Hudson County, New Jersey: Summary level: 150, state:34> county:017> tract:015802> block group:1 | 721 | 115 | 617 | 127 | 0 | None | None | None | None | None |
| Block Group 3, Census Tract 158.02, Hudson County, New Jersey: Summary level: 150, state:34> county:017> tract:015802> block group:3 | 581 | 65 | 95 | 72 | 0 | None | None | None | None | None |
Great! Now we have this data for further processing which I'll probably use in a future post.
Getting COVID data is quite easy from the COVID Tracking project. All we need is a URL which ww can get fromt their website: https://covidtracking.com/data/api
For instance, if we want the historic US values we can do the following query which will give us data from Janury 13,2020 until March 7,2021:
url = 'https://api.covidtracking.com/v1/us/daily.json'
r = requests.get(url)
daily_US = r.json()
url = 'https://api.covidtracking.com/v1/us/current.json'
r = requests.get(url)
current_US = r.json()
current_US
[{'date': 20210307,
'states': 56,
'positive': 28756489,
'negative': 74582825,
'pending': 11808,
'hospitalizedCurrently': 40199,
'hospitalizedCumulative': 776361,
'inIcuCurrently': 8134,
'inIcuCumulative': 45475,
'onVentilatorCurrently': 2802,
'onVentilatorCumulative': 4281,
'dateChecked': '2021-03-07T24:00:00Z',
'death': 515151,
'hospitalized': 776361,
'totalTestResults': 363825123,
'lastModified': '2021-03-07T24:00:00Z',
'recovered': None,
'total': 0,
'posNeg': 0,
'deathIncrease': 842,
'hospitalizedIncrease': 726,
'negativeIncrease': 131835,
'positiveIncrease': 41835,
'totalTestResultsIncrease': 1170059,
'hash': 'a80d0063822e251249fd9a44730c49cb23defd83'}]
We can get data for a specific state by writing a query of the following format:
https://api.covidtracking.com/v1/states/{state}/info.json
state = 'ny'
url = f'https://api.covidtracking.com/v1/states/{state}/daily.json'
r = requests.get(url)
nj_data = r.json()
We can also get COVID data for all states using the following URL: https://api.covidtracking.com/v1/states/daily.json.
Placing the result into a pandas dataframe only requires us to use theDataFrame function on our query.
df = pd.DataFrame(nj_data)
df
| date | state | positive | probableCases | negative | pending | totalTestResultsSource | totalTestResults | hospitalizedCurrently | hospitalizedCumulative | ... | dataQualityGrade | deathIncrease | hospitalizedIncrease | hash | commercialScore | negativeRegularScore | negativeScore | positiveScore | score | grade | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 20210307 | NY | 1681169 | None | None | NaN | totalTestEncountersViral | 39695100 | 4789.0 | None | ... | None | 59 | 0 | 8b1ad6376deb03844d4e5e3f615197da1b3a214e | 0 | 0 | 0 | 0 | 0 | |
| 1 | 20210306 | NY | 1674380 | None | None | NaN | totalTestEncountersViral | 39467332 | 4954.0 | None | ... | None | 79 | 0 | c7ae8ab7600d0db452429e5692968b185e90d1de | 0 | 0 | 0 | 0 | 0 | |
| 2 | 20210305 | NY | 1666733 | None | None | NaN | totalTestEncountersViral | 39194200 | 5034.0 | None | ... | None | 95 | 0 | 99bf166f33908a152c9badb3464d1899821323a5 | 0 | 0 | 0 | 0 | 0 | |
| 3 | 20210304 | NY | 1657777 | None | None | NaN | totalTestEncountersViral | 38897265 | 5177.0 | None | ... | None | 61 | 0 | 39d0cec8753202639bdd32abdd228cc6c891d862 | 0 | 0 | 0 | 0 | 0 | |
| 4 | 20210303 | NY | 1650184 | None | None | NaN | totalTestEncountersViral | 38627176 | 5323.0 | None | ... | None | 75 | 0 | 91cc3a4e0e57073b8997de61de8a6e385cf66599 | 0 | 0 | 0 | 0 | 0 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 366 | 20200306 | NY | 25 | None | None | 236.0 | totalTestEncountersViral | 122 | NaN | None | ... | None | 0 | 0 | 9e99ccc04e6d33397cc412ee1b0fbecddbaeecd8 | 0 | 0 | 0 | 0 | 0 | |
| 367 | 20200305 | NY | 3 | None | None | 24.0 | totalTestEncountersViral | 30 | NaN | None | ... | None | 0 | 0 | 04e82e80db72556a73999ead493c993e90b8969e | 0 | 0 | 0 | 0 | 0 | |
| 368 | 20200304 | NY | 1 | None | None | 24.0 | totalTestEncountersViral | 10 | NaN | None | ... | None | 0 | 0 | 3feb3bdb879194f81d11c8bbf994d8e3ea2a5252 | 0 | 0 | 0 | 0 | 0 | |
| 369 | 20200303 | NY | 1 | None | None | NaN | totalTestEncountersViral | 1 | NaN | None | ... | None | 0 | 0 | 9e3daed71a696c0c3fa30150ee6423a0a94de8f5 | 0 | 0 | 0 | 0 | 0 | |
| 370 | 20200302 | NY | 0 | None | None | NaN | totalTestEncountersViral | 0 | NaN | None | ... | None | 0 | 0 | b348e3592c0b30cb944b37ddbc88044ceeeadfc9 | 0 | 0 | 0 | 0 | 0 |
371 rows × 56 columns
I'm sure there's plenty of interesting stuff to analyze here for a future project and generate interesting time-series visualizations.
We can get information about Mars Rovers, Asteroids, Astronomy Picture of the Day, Exoplanet Archive, and Earth Polychromatic Imaging Camera (EPIC) amongst many other things from their API. You can register for an API quite easily here: https://api.nasa.gov/
api_key = 'uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'
I'm going to start by retrieving the Astronomy Picture of the Day (APOD). I'll write a little function that will take a date string in YYYY-MM-DD format, the NASA api_key, and string bool to determine whether we are extracting the HD version of the APOD. This function will output information about the APOD and display the image.
import urllib.request
import cv2
import numpy as np
import matplotlib.pyplot as plt
def fetchAPOD(api_key, date, hd):
URL_APOD = "https://api.nasa.gov/planetary/apod"
params = {
'api_key':api_key,
'date':date,
'hd': hd
}
response = requests.get(URL_APOD,params=params).json()
pp.pprint(response)
path2apod = response['url']
#This is just to display the album covers in the output
fig = plt.figure(figsize=(10,10))
req = urllib.request.urlopen(path2apod)
arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
img = cv2.imdecode(arr, -1) # 'Load it as it is'
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.show()
fetchAPOD(api_key, '2005-09-12', 'True')
{'copyright': 'Gemini Obs.',
'date': '2005-09-12',
'explanation': 'Is this one galaxy or two? The jumble of stars, gas, and '
'dust that is NGC 520 is now thought to incorporate the '
'remains of two separate galaxies. A combination of '
'observations and simulations indicate the NGC 520 is actually '
'the collision of two disk galaxies. Interesting features of '
'NGC 520 include an unfamiliar looking tail of stars at the '
'image bottom and a perhaps more familiar looking band of dust '
'running diagonally across the image center. A similar '
'looking collision might be expected were our disk Milky Way '
'Galaxy to collide with our large galactic neighbor Andromeda '
'(M31). The collision that defines NGC 520 started about 300 '
'million years ago and continues today. Although the speeds '
'of stars are fast, the distances are so vast that the '
'interacting pair will surely not change its shape noticeably '
'during our lifetimes. NGC 520, at visual magnitude 12, has '
'been noted to be one of the brightest interacting galaxies on '
'the sky, after interacting pairs of galaxies known as the '
'Antennae. NGC 520 was imaged above in spectacular fashion by '
'the Gemini Observatory in Hawaii, USA. Also known as Arp 157, '
'NGC 520 lies about 100 million light years distant, spans '
'about 100 thousand light years, and can be seen with a small '
'telescope toward the constellation of the Fish (Pisces).',
'hdurl': 'https://apod.nasa.gov/apod/image/0509/ngc520_gemini_big.jpg',
'media_type': 'image',
'service_version': 'v1',
'title': 'The Colliding Galaxies of NGC 520',
'url': 'https://apod.nasa.gov/apod/image/0509/ngc520_gemini.jpg'}
I think I'll write a little project for fun to randomly generate an APOD sometime soon :]
Next, I'll look into the Asteroid Near Earth Objects (ANEO) API. This API only allows data to be collected over a maximum span of 7 days. However, we could readily extract more data by enclosing our queries in a for loop, similar to how it was done with Spotify.
def fetchANEOWS(api_key,start_date,end_date):
URL_NeoFeed = "https://api.nasa.gov/neo/rest/v1/feed"
params = {
'api_key':api_key,
'start_date':start_date,
'end_date':end_date
}
response = requests.get(URL_NeoFeed,params=params).json()
pp.pprint(response)
return response
aneo = fetchANEOWS(api_key,'2022-08-16','2022-08-17')
{'element_count': 27,
'links': {'next': 'http://www.neowsapp.com/rest/v1/feed?start_date=2022-08-17&end_date=2022-08-18&detailed=false&api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH',
'prev': 'http://www.neowsapp.com/rest/v1/feed?start_date=2022-08-15&end_date=2022-08-16&detailed=false&api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH',
'self': 'http://www.neowsapp.com/rest/v1/feed?start_date=2022-08-16&end_date=2022-08-17&detailed=false&api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'near_earth_objects': {'2022-08-16': [{'absolute_magnitude_h': 18.05,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'19:05',
'epoch_date_close_approach': 1660676700000,
'miss_distance': {'astronomical': '0.2238852542',
'kilometers': '33492757.152728554',
'lunar': '87.0913638838',
'miles': '20811434.2556758852'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '63921.2642544193',
'kilometers_per_second': '17.7559067373',
'miles_per_hour': '39718.1877807529'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 4786.5767834811,
'estimated_diameter_min': 2140.6222134772},
'kilometers': {'estimated_diameter_max': 1.4589485569,
'estimated_diameter_min': 0.6524616298},
'meters': {'estimated_diameter_max': 1458.9485569187,
'estimated_diameter_min': 652.4616297891},
'miles': {'estimated_diameter_max': 0.9065483238,
'estimated_diameter_min': 0.4054207354}},
'id': '2159928',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/2159928?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '159928 (2005 CV69)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=2159928',
'neo_reference_id': '2159928'},
{'absolute_magnitude_h': 20.29,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'01:51',
'epoch_date_close_approach': 1660614660000,
'miss_distance': {'astronomical': '0.3948717663',
'kilometers': '59071975.161617781',
'lunar': '153.6051170907',
'miles': '36705623.3030601378'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '70023.6554030608',
'kilometers_per_second': '19.4510153897',
'miles_per_hour': '43509.9763253074'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 1706.1807197036,
'estimated_diameter_min': 763.0272142313},
'kilometers': {'estimated_diameter_max': 0.5200438667,
'estimated_diameter_min': 0.2325706875},
'meters': {'estimated_diameter_max': 520.0438667242,
'estimated_diameter_min': 232.5706874555},
'miles': {'estimated_diameter_max': 0.3231401775,
'estimated_diameter_min': 0.1445126806}},
'id': '2445974',
'is_potentially_hazardous_asteroid': True,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/2445974?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '445974 (2013 BJ18)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=2445974',
'neo_reference_id': '2445974'},
{'absolute_magnitude_h': 20.2,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'12:39',
'epoch_date_close_approach': 1660653540000,
'miss_distance': {'astronomical': '0.109943137',
'kilometers': '16447259.11631819',
'lunar': '42.767880293',
'miles': '10219852.914003422'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '47839.62239667',
'kilometers_per_second': '13.2887839991',
'miles_per_hour': '29725.6809275309'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 1778.3819018419,
'estimated_diameter_min': 795.3165644948},
'kilometers': {'estimated_diameter_max': 0.5420507863,
'estimated_diameter_min': 0.2424124811},
'meters': {'estimated_diameter_max': 542.0507863358,
'estimated_diameter_min': 242.4124811008},
'miles': {'estimated_diameter_max': 0.3368146392,
'estimated_diameter_min': 0.1506280858}},
'id': '3290881',
'is_potentially_hazardous_asteroid': True,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3290881?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2005 SQ)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3290881',
'neo_reference_id': '3290881'},
{'absolute_magnitude_h': 22.6,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'14:43',
'epoch_date_close_approach': 1660660980000,
'miss_distance': {'astronomical': '0.3344201149',
'kilometers': '50028536.874195263',
'lunar': '130.0894246961',
'miles': '31086291.3231421094'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '58490.6495788559',
'kilometers_per_second': '16.2474026608',
'miles_per_hour': '36343.8150119285'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 588.8775935812,
'estimated_diameter_min': 263.3540659348},
'kilometers': {'estimated_diameter_max': 0.1794898848,
'estimated_diameter_min': 0.0802703167},
'meters': {'estimated_diameter_max': 179.4898847799,
'estimated_diameter_min': 80.2703167283},
'miles': {'estimated_diameter_max': 0.1115298092,
'estimated_diameter_min': 0.049877647}},
'id': '3560817',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3560817?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2011 FS29)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3560817',
'neo_reference_id': '3560817'},
{'absolute_magnitude_h': 21.3,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'14:24',
'epoch_date_close_approach': 1660659840000,
'miss_distance': {'astronomical': '0.2994940718',
'kilometers': '44803675.218907066',
'lunar': '116.5031939302',
'miles': '27839712.8363908708'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '52802.968399447',
'kilometers_per_second': '14.6674912221',
'miles_per_hour': '32809.7111146453'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 1071.581062656,
'estimated_diameter_min': 479.2256199},
'kilometers': {'estimated_diameter_max': 0.3266178974,
'estimated_diameter_min': 0.1460679643},
'meters': {'estimated_diameter_max': 326.6178974458,
'estimated_diameter_min': 146.0679642714},
'miles': {'estimated_diameter_max': 0.2029508896,
'estimated_diameter_min': 0.090762397}},
'id': '3723955',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3723955?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2015 NK3)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3723955',
'neo_reference_id': '3723955'},
{'absolute_magnitude_h': 21.3,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'06:33',
'epoch_date_close_approach': 1660631580000,
'miss_distance': {'astronomical': '0.2891952171',
'kilometers': '43262988.492347577',
'lunar': '112.4969394519',
'miles': '26882374.4968754826'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '5567.3396910941',
'kilometers_per_second': '1.5464832475',
'miles_per_hour': '3459.3283763912'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 1071.581062656,
'estimated_diameter_min': 479.2256199},
'kilometers': {'estimated_diameter_max': 0.3266178974,
'estimated_diameter_min': 0.1460679643},
'meters': {'estimated_diameter_max': 326.6178974458,
'estimated_diameter_min': 146.0679642714},
'miles': {'estimated_diameter_max': 0.2029508896,
'estimated_diameter_min': 0.090762397}},
'id': '3778647',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3778647?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2017 OR18)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3778647',
'neo_reference_id': '3778647'},
{'absolute_magnitude_h': 21.1,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'23:23',
'epoch_date_close_approach': 1660692180000,
'miss_distance': {'astronomical': '0.2136063756',
'kilometers': '31955058.808179972',
'lunar': '83.0928801084',
'miles': '19855952.8106367336'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '96210.2103546877',
'kilometers_per_second': '26.7250584319',
'miles_per_hour': '59781.2832062225'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 1174.9652706022,
'estimated_diameter_min': 525.4604432536},
'kilometers': {'estimated_diameter_max': 0.358129403,
'estimated_diameter_min': 0.160160338},
'meters': {'estimated_diameter_max': 358.1294030194,
'estimated_diameter_min': 160.1603379786},
'miles': {'estimated_diameter_max': 0.2225312253,
'estimated_diameter_min': 0.0995189894}},
'id': '3799743',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3799743?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2018 DT1)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3799743',
'neo_reference_id': '3799743'},
{'absolute_magnitude_h': 24.2,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'23:27',
'epoch_date_close_approach': 1660692420000,
'miss_distance': {'astronomical': '0.2441899855',
'kilometers': '36530301.706130885',
'lunar': '94.9899043595',
'miles': '22698876.919280813'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '24767.4056852611',
'kilometers_per_second': '6.8798349126',
'miles_per_hour': '15389.5027159335'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 281.8545369825,
'estimated_diameter_min': 126.0491808919},
'kilometers': {'estimated_diameter_max': 0.0859092601,
'estimated_diameter_min': 0.0384197891},
'meters': {'estimated_diameter_max': 85.9092601232,
'estimated_diameter_min': 38.4197891064},
'miles': {'estimated_diameter_max': 0.0533815229,
'estimated_diameter_min': 0.0238729428}},
'id': '3826623',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3826623?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2018 PV7)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3826623',
'neo_reference_id': '3826623'},
{'absolute_magnitude_h': 22.61,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'12:10',
'epoch_date_close_approach': 1660651800000,
'miss_distance': {'astronomical': '0.2582663589',
'kilometers': '38636097.184095543',
'lunar': '100.4656136121',
'miles': '24007357.5542347734'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '28395.3301493837',
'kilometers_per_second': '7.8875917082',
'miles_per_hour': '17643.7538919879'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 586.1719468077,
'estimated_diameter_min': 262.1440639131},
'kilometers': {'estimated_diameter_max': 0.1786652037,
'estimated_diameter_min': 0.0799015081},
'meters': {'estimated_diameter_max': 178.6652036697,
'estimated_diameter_min': 79.9015081239},
'miles': {'estimated_diameter_max': 0.1110173763,
'estimated_diameter_min': 0.04964848}},
'id': '3843359',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3843359?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2019 OH1)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3843359',
'neo_reference_id': '3843359'},
{'absolute_magnitude_h': 20.8,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'17:56',
'epoch_date_close_approach': 1660672560000,
'miss_distance': {'astronomical': '0.4750418782',
'kilometers': '71065253.139519434',
'lunar': '184.7912906198',
'miles': '44157900.6718348292'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '21721.6268160229',
'kilometers_per_second': '6.0337852267',
'miles_per_hour': '13496.9741735449'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 1349.040630575,
'estimated_diameter_min': 603.309310875},
'kilometers': {'estimated_diameter_max': 0.411187571,
'estimated_diameter_min': 0.1838886721},
'meters': {'estimated_diameter_max': 411.1875710413,
'estimated_diameter_min': 183.8886720703},
'miles': {'estimated_diameter_max': 0.2555000322,
'estimated_diameter_min': 0.1142630881}},
'id': '54050972',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/54050972?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2020 OC7)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54050972',
'neo_reference_id': '54050972'},
{'absolute_magnitude_h': 20.6,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'10:48',
'epoch_date_close_approach': 1660646880000,
'miss_distance': {'astronomical': '0.2304836679',
'kilometers': '34479865.787627373',
'lunar': '89.6581468131',
'miles': '21424795.1194808274'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '91559.042679109',
'kilometers_per_second': '25.4330674109',
'miles_per_hour': '56891.2284913608'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 1479.1936371367,
'estimated_diameter_min': 661.5155049046},
'kilometers': {'estimated_diameter_max': 0.4508582062,
'estimated_diameter_min': 0.2016299194},
'meters': {'estimated_diameter_max': 450.8582061718,
'estimated_diameter_min': 201.6299194428},
'miles': {'estimated_diameter_max': 0.2801502144,
'estimated_diameter_min': 0.1252869847}},
'id': '54075323',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/54075323?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2020 TN8)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54075323',
'neo_reference_id': '54075323'},
{'absolute_magnitude_h': 29.6,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'04:07',
'epoch_date_close_approach': 1660622820000,
'miss_distance': {'astronomical': '0.1876322222',
'kilometers': '28069380.784486714',
'lunar': '72.9889344358',
'miles': '17441504.4461720932'},
'orbiting_body': 'Earth',
'relative_velocity': {'kilometers_per_hour': '21111.7259858685',
'kilometers_per_second': '5.8643683294',
'miles_per_hour': '13118.0055160526'}}],
'estimated_diameter': {'feet': {'estimated_diameter_max': 23.4436392583,
'estimated_diameter_min': 10.4843142043},
'kilometers': {'estimated_diameter_max': 0.007145621,
'estimated_diameter_min': 0.0031956189},
'meters': {'estimated_diameter_max': 7.1456210173,
'estimated_diameter_min': 3.1956188672},
'miles': {'estimated_diameter_max': 0.0044400817,
'estimated_diameter_min': 0.0019856649}},
'id': '54106530',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/54106530?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2021 BP1)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54106530',
'neo_reference_id': '54106530'},
{'absolute_magnitude_h': 23.92,
'close_approach_data': [{'close_approach_date': '2022-08-16',
'close_approach_date_full': '2022-Aug-16 '
'18:06',
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'miss_distance': {'astronomical': '0.4361941952',
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'estimated_diameter_min': 47.4832053008},
'kilometers': {'estimated_diameter_max': 0.0323623447,
'estimated_diameter_min': 0.0144728805},
'meters': {'estimated_diameter_max': 32.3623446563,
'estimated_diameter_min': 14.4728805126},
'miles': {'estimated_diameter_max': 0.0201090225,
'estimated_diameter_min': 0.0089930282}},
'id': '54265877',
'is_potentially_hazardous_asteroid': False,
'is_sentry_object': False,
'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/54265877?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'name': '(2022 FM2)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54265877',
'neo_reference_id': '54265877'}]}}
Yikes! This is a pretty complicated looking nested json! The data we want is contained in the 'near_earth_objects' key. This key then maps to the dates we queried data for. I tried doing a simple flattening as well as normalizing the json data in order to put the data into a pandas dataframe but wasn't successful. However, the following function can be used to recursively look through our desired level.
def flatten_json(nested_json: dict, exclude: list=['']) -> dict:
"""
Flatten a list of nested dicts.
"""
out = dict()
def flatten(x: (list, dict, str), name: str='', exclude=exclude):
if type(x) is dict:
for a in x:
if a not in exclude:
flatten(x[a], f'{name}{a}_')
elif type(x) is list:
i = 0
for a in x:
flatten(a, f'{name}{i}_')
i += 1
else:
out[name[:-1]] = x
flatten(nested_json)
return out
The level we want is here
aneo['near_earth_objects']['2022-08-17']
[{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/2285638?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '2285638',
'neo_reference_id': '2285638',
'name': '285638 (2000 SO10)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=2285638',
'absolute_magnitude_h': 17.53,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.8290008515,
'estimated_diameter_max': 1.8537022573},
'meters': {'estimated_diameter_min': 829.0008514522,
'estimated_diameter_max': 1853.7022572523},
'miles': {'estimated_diameter_min': 0.5151170881,
'estimated_diameter_max': 1.1518368253},
'feet': {'estimated_diameter_min': 2719.8191534783,
'estimated_diameter_max': 6081.7005136835}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 23:06',
'epoch_date_close_approach': 1660777560000,
'relative_velocity': {'kilometers_per_second': '21.6193413097',
'kilometers_per_hour': '77829.6287147987',
'miles_per_hour': '48360.3045755925'},
'miss_distance': {'astronomical': '0.4753897322',
'lunar': '184.9266058258',
'kilometers': '71117291.356990414',
'miles': '44190235.7207751532'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3330687?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '3330687',
'neo_reference_id': '3330687',
'name': '(2006 HU50)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3330687',
'absolute_magnitude_h': 24.7,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0305179233,
'estimated_diameter_max': 0.0682401509},
'meters': {'estimated_diameter_min': 30.5179232594,
'estimated_diameter_max': 68.2401509401},
'miles': {'estimated_diameter_min': 0.0189629525,
'estimated_diameter_max': 0.0424024508},
'feet': {'estimated_diameter_min': 100.1244233463,
'estimated_diameter_max': 223.8850168104}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 07:33',
'epoch_date_close_approach': 1660721580000,
'relative_velocity': {'kilometers_per_second': '12.8345824666',
'kilometers_per_hour': '46204.4968798295',
'miles_per_hour': '28709.6775195807'},
'miss_distance': {'astronomical': '0.2738866136',
'lunar': '106.5418926904',
'kilometers': '40972854.016073032',
'miles': '25459350.9197985616'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3532394?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '3532394',
'neo_reference_id': '3532394',
'name': '(2010 MP1)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3532394',
'absolute_magnitude_h': 21.4,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.1394938229,
'estimated_diameter_max': 0.3119176705},
'meters': {'estimated_diameter_min': 139.4938229344,
'estimated_diameter_max': 311.9176705226},
'miles': {'estimated_diameter_min': 0.0866774163,
'estimated_diameter_max': 0.1938165949},
'feet': {'estimated_diameter_min': 457.6569140361,
'estimated_diameter_max': 1023.3519701574}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 08:46',
'epoch_date_close_approach': 1660725960000,
'relative_velocity': {'kilometers_per_second': '12.6446138738',
'kilometers_per_hour': '45520.6099457225',
'miles_per_hour': '28284.7367743296'},
'miss_distance': {'astronomical': '0.390291961',
'lunar': '151.823572829',
'kilometers': '58386846.04372307',
'miles': '36279903.810075566'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3690061?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '3690061',
'neo_reference_id': '3690061',
'name': '(2014 SH144)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3690061',
'absolute_magnitude_h': 25.0,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.02658,
'estimated_diameter_max': 0.0594346868},
'meters': {'estimated_diameter_min': 26.58,
'estimated_diameter_max': 59.4346868419},
'miles': {'estimated_diameter_min': 0.0165160412,
'estimated_diameter_max': 0.0369309908},
'feet': {'estimated_diameter_min': 87.2047272,
'estimated_diameter_max': 194.9956979785}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 06:15',
'epoch_date_close_approach': 1660716900000,
'relative_velocity': {'kilometers_per_second': '12.611330367',
'kilometers_per_hour': '45400.7893213213',
'miles_per_hour': '28210.2848980177'},
'miss_distance': {'astronomical': '0.4146458219',
'lunar': '161.2972247191',
'kilometers': '62030131.760639353',
'miles': '38543736.5792469514'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3714001?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '3714001',
'neo_reference_id': '3714001',
'name': '(2015 FN35)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3714001',
'absolute_magnitude_h': 23.7,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0483676488,
'estimated_diameter_max': 0.1081533507},
'meters': {'estimated_diameter_min': 48.3676488219,
'estimated_diameter_max': 108.1533506775},
'miles': {'estimated_diameter_min': 0.0300542543,
'estimated_diameter_max': 0.0672033557},
'feet': {'estimated_diameter_min': 158.6865169607,
'estimated_diameter_max': 354.8338390368}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 10:34',
'epoch_date_close_approach': 1660732440000,
'relative_velocity': {'kilometers_per_second': '10.8605045101',
'kilometers_per_hour': '39097.8162363992',
'miles_per_hour': '24293.8625386671'},
'miss_distance': {'astronomical': '0.0529267402',
'lunar': '20.5885019378',
'kilometers': '7917727.599963374',
'miles': '4919847.7942436012'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3744834?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '3744834',
'neo_reference_id': '3744834',
'name': '(2016 DW1)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3744834',
'absolute_magnitude_h': 25.9,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0175612318,
'estimated_diameter_max': 0.0392681082},
'meters': {'estimated_diameter_min': 17.561231848,
'estimated_diameter_max': 39.2681081809},
'miles': {'estimated_diameter_min': 0.0109120402,
'estimated_diameter_max': 0.0244000636},
'feet': {'estimated_diameter_min': 57.6155918963,
'estimated_diameter_max': 128.8323800441}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 06:31',
'epoch_date_close_approach': 1660717860000,
'relative_velocity': {'kilometers_per_second': '16.3667287052',
'kilometers_per_hour': '58920.2233387679',
'miles_per_hour': '36610.7354406916'},
'miss_distance': {'astronomical': '0.3409988372',
'lunar': '132.6485476708',
'kilometers': '51012699.717596764',
'miles': '31697821.7569887832'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3797908?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '3797908',
'neo_reference_id': '3797908',
'name': '(2018 BK5)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3797908',
'absolute_magnitude_h': 23.1,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.063760979,
'estimated_diameter_max': 0.1425738833},
'meters': {'estimated_diameter_min': 63.7609789875,
'estimated_diameter_max': 142.5738833281},
'miles': {'estimated_diameter_min': 0.0396192233,
'estimated_diameter_max': 0.0885912765},
'feet': {'estimated_diameter_min': 209.1895703015,
'estimated_diameter_max': 467.7620993781}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 05:18',
'epoch_date_close_approach': 1660713480000,
'relative_velocity': {'kilometers_per_second': '15.2860846594',
'kilometers_per_hour': '55029.9047739469',
'miles_per_hour': '34193.4427746783'},
'miss_distance': {'astronomical': '0.4633320661',
'lunar': '180.2361737129',
'kilometers': '69313490.191259207',
'miles': '43069405.6499707766'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/3825100?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '3825100',
'neo_reference_id': '3825100',
'name': '(2018 LC1)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3825100',
'absolute_magnitude_h': 24.9,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0278326768,
'estimated_diameter_max': 0.0622357573},
'meters': {'estimated_diameter_min': 27.8326768072,
'estimated_diameter_max': 62.2357573367},
'miles': {'estimated_diameter_min': 0.0172944182,
'estimated_diameter_max': 0.0386714948},
'feet': {'estimated_diameter_min': 91.3145593761,
'estimated_diameter_max': 204.1855621004}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 04:58',
'epoch_date_close_approach': 1660712280000,
'relative_velocity': {'kilometers_per_second': '6.5806137697',
'kilometers_per_hour': '23690.2095709795',
'miles_per_hour': '14720.1749414789'},
'miss_distance': {'astronomical': '0.3892544769',
'lunar': '151.4199915141',
'kilometers': '58231640.632204203',
'miles': '36183463.6393478814'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/54048888?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '54048888',
'neo_reference_id': '54048888',
'name': '(2020 OK1)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54048888',
'absolute_magnitude_h': 23.9,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.04411182,
'estimated_diameter_max': 0.0986370281},
'meters': {'estimated_diameter_min': 44.1118199997,
'estimated_diameter_max': 98.6370281305},
'miles': {'estimated_diameter_min': 0.0274098057,
'estimated_diameter_max': 0.0612901888},
'feet': {'estimated_diameter_min': 144.7238235278,
'estimated_diameter_max': 323.6123073718}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 06:25',
'epoch_date_close_approach': 1660717500000,
'relative_velocity': {'kilometers_per_second': '7.2317107133',
'kilometers_per_hour': '26034.1585677636',
'miles_per_hour': '16176.6136945085'},
'miss_distance': {'astronomical': '0.1938562821',
'lunar': '75.4100937369',
'kilometers': '29000486.888279127',
'miles': '18020066.9507688726'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/54053722?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '54053722',
'neo_reference_id': '54053722',
'name': '(2020 RC)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54053722',
'absolute_magnitude_h': 22.0,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.1058168859,
'estimated_diameter_max': 0.2366137501},
'meters': {'estimated_diameter_min': 105.8168859331,
'estimated_diameter_max': 236.6137501138},
'miles': {'estimated_diameter_min': 0.0657515442,
'estimated_diameter_max': 0.1470249225},
'feet': {'estimated_diameter_min': 347.1682720448,
'estimated_diameter_max': 776.2918559233}},
'is_potentially_hazardous_asteroid': True,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 15:49',
'epoch_date_close_approach': 1660751340000,
'relative_velocity': {'kilometers_per_second': '22.0650199131',
'kilometers_per_hour': '79434.0716872817',
'miles_per_hour': '49357.243043689'},
'miss_distance': {'astronomical': '0.3826928775',
'lunar': '148.8675293475',
'kilometers': '57250039.338170925',
'miles': '35573524.878471765'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/54123272?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '54123272',
'neo_reference_id': '54123272',
'name': '(2021 DC1)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54123272',
'absolute_magnitude_h': 26.24,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0150160248,
'estimated_diameter_max': 0.0335768522},
'meters': {'estimated_diameter_min': 15.0160247905,
'estimated_diameter_max': 33.5768521834},
'miles': {'estimated_diameter_min': 0.0093305223,
'estimated_diameter_max': 0.0208636822},
'feet': {'estimated_diameter_min': 49.2651747737,
'estimated_diameter_max': 110.1602797174}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 22:34',
'epoch_date_close_approach': 1660775640000,
'relative_velocity': {'kilometers_per_second': '6.9126875471',
'kilometers_per_hour': '24885.6751694751',
'miles_per_hour': '15462.9907740552'},
'miss_distance': {'astronomical': '0.0924481569',
'lunar': '35.9623330341',
'kilometers': '13830047.357665803',
'miles': '8593592.9378539614'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/54265632?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '54265632',
'neo_reference_id': '54265632',
'name': '(2022 FR1)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54265632',
'absolute_magnitude_h': 28.12,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0063176415,
'estimated_diameter_max': 0.0141266758},
'meters': {'estimated_diameter_min': 6.3176414818,
'estimated_diameter_max': 14.1266758109},
'miles': {'estimated_diameter_min': 0.0039255992,
'estimated_diameter_max': 0.0087779067},
'feet': {'estimated_diameter_min': 20.7271708793,
'estimated_diameter_max': 46.3473630674}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 23:36',
'epoch_date_close_approach': 1660779360000,
'relative_velocity': {'kilometers_per_second': '5.5363209356',
'kilometers_per_hour': '19930.7553682022',
'miles_per_hour': '12384.1963008699'},
'miss_distance': {'astronomical': '0.4323786926',
'lunar': '168.1953114214',
'kilometers': '64682931.446344762',
'miles': '40192109.8678592356'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False},
{'links': {'self': 'http://www.neowsapp.com/rest/v1/neo/54265877?api_key=uIuYlzCW70zB1F9oRcBUIIzhpM9m4V2neOwhn9iH'},
'id': '54265877',
'neo_reference_id': '54265877',
'name': '(2022 FM2)',
'nasa_jpl_url': 'http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54265877',
'absolute_magnitude_h': 26.32,
'estimated_diameter': {'kilometers': {'estimated_diameter_min': 0.0144728805,
'estimated_diameter_max': 0.0323623447},
'meters': {'estimated_diameter_min': 14.4728805126,
'estimated_diameter_max': 32.3623446563},
'miles': {'estimated_diameter_min': 0.0089930282,
'estimated_diameter_max': 0.0201090225},
'feet': {'estimated_diameter_min': 47.4832053008,
'estimated_diameter_max': 106.1756748422}},
'is_potentially_hazardous_asteroid': False,
'close_approach_data': [{'close_approach_date': '2022-08-17',
'close_approach_date_full': '2022-Aug-17 14:35',
'epoch_date_close_approach': 1660746900000,
'relative_velocity': {'kilometers_per_second': '6.7095921222',
'kilometers_per_hour': '24154.531639754',
'miles_per_hour': '15008.6866180461'},
'miss_distance': {'astronomical': '0.1988044094',
'lunar': '77.3349152566',
'kilometers': '29740716.192847978',
'miles': '18480024.1121309764'},
'orbiting_body': 'Earth'}],
'is_sentry_object': False}]
Then we can make our data frame by applying the function above in order to obtain the result below :]
df = pd.DataFrame([flatten_json(x) for x in aneo['near_earth_objects']['2022-08-17']])
df
| links_self | id | neo_reference_id | name | nasa_jpl_url | absolute_magnitude_h | estimated_diameter_kilometers_estimated_diameter_min | estimated_diameter_kilometers_estimated_diameter_max | estimated_diameter_meters_estimated_diameter_min | estimated_diameter_meters_estimated_diameter_max | ... | close_approach_data_0_epoch_date_close_approach | close_approach_data_0_relative_velocity_kilometers_per_second | close_approach_data_0_relative_velocity_kilometers_per_hour | close_approach_data_0_relative_velocity_miles_per_hour | close_approach_data_0_miss_distance_astronomical | close_approach_data_0_miss_distance_lunar | close_approach_data_0_miss_distance_kilometers | close_approach_data_0_miss_distance_miles | close_approach_data_0_orbiting_body | is_sentry_object | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | http://www.neowsapp.com/rest/v1/neo/2285638?ap... | 2285638 | 2285638 | 285638 (2000 SO10) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=2285638 | 17.53 | 0.829001 | 1.853702 | 829.000851 | 1853.702257 | ... | 1660777560000 | 21.6193413097 | 77829.6287147987 | 48360.3045755925 | 0.4753897322 | 184.9266058258 | 71117291.356990414 | 44190235.7207751532 | Earth | False |
| 1 | http://www.neowsapp.com/rest/v1/neo/3330687?ap... | 3330687 | 3330687 | (2006 HU50) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3330687 | 24.70 | 0.030518 | 0.068240 | 30.517923 | 68.240151 | ... | 1660721580000 | 12.8345824666 | 46204.4968798295 | 28709.6775195807 | 0.2738866136 | 106.5418926904 | 40972854.016073032 | 25459350.9197985616 | Earth | False |
| 2 | http://www.neowsapp.com/rest/v1/neo/3532394?ap... | 3532394 | 3532394 | (2010 MP1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3532394 | 21.40 | 0.139494 | 0.311918 | 139.493823 | 311.917671 | ... | 1660725960000 | 12.6446138738 | 45520.6099457225 | 28284.7367743296 | 0.390291961 | 151.823572829 | 58386846.04372307 | 36279903.810075566 | Earth | False |
| 3 | http://www.neowsapp.com/rest/v1/neo/3690061?ap... | 3690061 | 3690061 | (2014 SH144) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3690061 | 25.00 | 0.026580 | 0.059435 | 26.580000 | 59.434687 | ... | 1660716900000 | 12.611330367 | 45400.7893213213 | 28210.2848980177 | 0.4146458219 | 161.2972247191 | 62030131.760639353 | 38543736.5792469514 | Earth | False |
| 4 | http://www.neowsapp.com/rest/v1/neo/3714001?ap... | 3714001 | 3714001 | (2015 FN35) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3714001 | 23.70 | 0.048368 | 0.108153 | 48.367649 | 108.153351 | ... | 1660732440000 | 10.8605045101 | 39097.8162363992 | 24293.8625386671 | 0.0529267402 | 20.5885019378 | 7917727.599963374 | 4919847.7942436012 | Earth | False |
| 5 | http://www.neowsapp.com/rest/v1/neo/3744834?ap... | 3744834 | 3744834 | (2016 DW1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3744834 | 25.90 | 0.017561 | 0.039268 | 17.561232 | 39.268108 | ... | 1660717860000 | 16.3667287052 | 58920.2233387679 | 36610.7354406916 | 0.3409988372 | 132.6485476708 | 51012699.717596764 | 31697821.7569887832 | Earth | False |
| 6 | http://www.neowsapp.com/rest/v1/neo/3797908?ap... | 3797908 | 3797908 | (2018 BK5) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3797908 | 23.10 | 0.063761 | 0.142574 | 63.760979 | 142.573883 | ... | 1660713480000 | 15.2860846594 | 55029.9047739469 | 34193.4427746783 | 0.4633320661 | 180.2361737129 | 69313490.191259207 | 43069405.6499707766 | Earth | False |
| 7 | http://www.neowsapp.com/rest/v1/neo/3825100?ap... | 3825100 | 3825100 | (2018 LC1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3825100 | 24.90 | 0.027833 | 0.062236 | 27.832677 | 62.235757 | ... | 1660712280000 | 6.5806137697 | 23690.2095709795 | 14720.1749414789 | 0.3892544769 | 151.4199915141 | 58231640.632204203 | 36183463.6393478814 | Earth | False |
| 8 | http://www.neowsapp.com/rest/v1/neo/54048888?a... | 54048888 | 54048888 | (2020 OK1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54048888 | 23.90 | 0.044112 | 0.098637 | 44.111820 | 98.637028 | ... | 1660717500000 | 7.2317107133 | 26034.1585677636 | 16176.6136945085 | 0.1938562821 | 75.4100937369 | 29000486.888279127 | 18020066.9507688726 | Earth | False |
| 9 | http://www.neowsapp.com/rest/v1/neo/54053722?a... | 54053722 | 54053722 | (2020 RC) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54053722 | 22.00 | 0.105817 | 0.236614 | 105.816886 | 236.613750 | ... | 1660751340000 | 22.0650199131 | 79434.0716872817 | 49357.243043689 | 0.3826928775 | 148.8675293475 | 57250039.338170925 | 35573524.878471765 | Earth | False |
| 10 | http://www.neowsapp.com/rest/v1/neo/54123272?a... | 54123272 | 54123272 | (2021 DC1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54123272 | 26.24 | 0.015016 | 0.033577 | 15.016025 | 33.576852 | ... | 1660775640000 | 6.9126875471 | 24885.6751694751 | 15462.9907740552 | 0.0924481569 | 35.9623330341 | 13830047.357665803 | 8593592.9378539614 | Earth | False |
| 11 | http://www.neowsapp.com/rest/v1/neo/54265632?a... | 54265632 | 54265632 | (2022 FR1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54265632 | 28.12 | 0.006318 | 0.014127 | 6.317641 | 14.126676 | ... | 1660779360000 | 5.5363209356 | 19930.7553682022 | 12384.1963008699 | 0.4323786926 | 168.1953114214 | 64682931.446344762 | 40192109.8678592356 | Earth | False |
| 12 | http://www.neowsapp.com/rest/v1/neo/54265877?a... | 54265877 | 54265877 | (2022 FM2) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54265877 | 26.32 | 0.014473 | 0.032362 | 14.472881 | 32.362345 | ... | 1660746900000 | 6.7095921222 | 24154.531639754 | 15008.6866180461 | 0.1988044094 | 77.3349152566 | 29740716.192847978 | 18480024.1121309764 | Earth | False |
13 rows × 27 columns
As I did this it hit me... why don't I simply loop over the json starting at this level? So I loaded the json file starting at that level and... voila!
aneo_df = pd.DataFrame(aneo['near_earth_objects']['2022-08-17'])
aneo_df
| links | id | neo_reference_id | name | nasa_jpl_url | absolute_magnitude_h | estimated_diameter | is_potentially_hazardous_asteroid | close_approach_data | is_sentry_object | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 2285638 | 2285638 | 285638 (2000 SO10) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=2285638 | 17.53 | {'kilometers': {'estimated_diameter_min': 0.82... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 1 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3330687 | 3330687 | (2006 HU50) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3330687 | 24.70 | {'kilometers': {'estimated_diameter_min': 0.03... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 2 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3532394 | 3532394 | (2010 MP1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3532394 | 21.40 | {'kilometers': {'estimated_diameter_min': 0.13... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 3 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3690061 | 3690061 | (2014 SH144) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3690061 | 25.00 | {'kilometers': {'estimated_diameter_min': 0.02... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 4 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3714001 | 3714001 | (2015 FN35) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3714001 | 23.70 | {'kilometers': {'estimated_diameter_min': 0.04... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 5 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3744834 | 3744834 | (2016 DW1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3744834 | 25.90 | {'kilometers': {'estimated_diameter_min': 0.01... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 6 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3797908 | 3797908 | (2018 BK5) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3797908 | 23.10 | {'kilometers': {'estimated_diameter_min': 0.06... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 7 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3825100 | 3825100 | (2018 LC1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3825100 | 24.90 | {'kilometers': {'estimated_diameter_min': 0.02... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 8 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54048888 | 54048888 | (2020 OK1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54048888 | 23.90 | {'kilometers': {'estimated_diameter_min': 0.04... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 9 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54053722 | 54053722 | (2020 RC) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54053722 | 22.00 | {'kilometers': {'estimated_diameter_min': 0.10... | True | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 10 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54123272 | 54123272 | (2021 DC1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54123272 | 26.24 | {'kilometers': {'estimated_diameter_min': 0.01... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 11 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54265632 | 54265632 | (2022 FR1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54265632 | 28.12 | {'kilometers': {'estimated_diameter_min': 0.00... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 12 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54265877 | 54265877 | (2022 FM2) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54265877 | 26.32 | {'kilometers': {'estimated_diameter_min': 0.01... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
The data there is for just one day. But now we can easily loop through the days and concatenate our dataframes! I only did a query for two days so my date list is as follows
date_list = ['2022-08-17','2022-08-16']
tot_aneo_list = []
for date in date_list:
aneo_df = pd.DataFrame(aneo['near_earth_objects'][date])
tot_aneo_list.append(aneo_df)
aneo_tot_df = pd.concat(tot_aneo_list, ignore_index = True)
aneo_tot_df
| links | id | neo_reference_id | name | nasa_jpl_url | absolute_magnitude_h | estimated_diameter | is_potentially_hazardous_asteroid | close_approach_data | is_sentry_object | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 2285638 | 2285638 | 285638 (2000 SO10) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=2285638 | 17.53 | {'kilometers': {'estimated_diameter_min': 0.82... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 1 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3330687 | 3330687 | (2006 HU50) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3330687 | 24.70 | {'kilometers': {'estimated_diameter_min': 0.03... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 2 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3532394 | 3532394 | (2010 MP1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3532394 | 21.40 | {'kilometers': {'estimated_diameter_min': 0.13... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 3 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3690061 | 3690061 | (2014 SH144) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3690061 | 25.00 | {'kilometers': {'estimated_diameter_min': 0.02... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 4 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3714001 | 3714001 | (2015 FN35) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3714001 | 23.70 | {'kilometers': {'estimated_diameter_min': 0.04... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 5 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3744834 | 3744834 | (2016 DW1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3744834 | 25.90 | {'kilometers': {'estimated_diameter_min': 0.01... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 6 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3797908 | 3797908 | (2018 BK5) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3797908 | 23.10 | {'kilometers': {'estimated_diameter_min': 0.06... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 7 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3825100 | 3825100 | (2018 LC1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3825100 | 24.90 | {'kilometers': {'estimated_diameter_min': 0.02... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 8 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54048888 | 54048888 | (2020 OK1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54048888 | 23.90 | {'kilometers': {'estimated_diameter_min': 0.04... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 9 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54053722 | 54053722 | (2020 RC) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54053722 | 22.00 | {'kilometers': {'estimated_diameter_min': 0.10... | True | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 10 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54123272 | 54123272 | (2021 DC1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54123272 | 26.24 | {'kilometers': {'estimated_diameter_min': 0.01... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 11 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54265632 | 54265632 | (2022 FR1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54265632 | 28.12 | {'kilometers': {'estimated_diameter_min': 0.00... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 12 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54265877 | 54265877 | (2022 FM2) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54265877 | 26.32 | {'kilometers': {'estimated_diameter_min': 0.01... | False | [{'close_approach_date': '2022-08-17', 'close_... | False |
| 13 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 2159928 | 2159928 | 159928 (2005 CV69) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=2159928 | 18.05 | {'kilometers': {'estimated_diameter_min': 0.65... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 14 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 2445974 | 2445974 | 445974 (2013 BJ18) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=2445974 | 20.29 | {'kilometers': {'estimated_diameter_min': 0.23... | True | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 15 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3290881 | 3290881 | (2005 SQ) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3290881 | 20.20 | {'kilometers': {'estimated_diameter_min': 0.24... | True | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 16 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3560817 | 3560817 | (2011 FS29) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3560817 | 22.60 | {'kilometers': {'estimated_diameter_min': 0.08... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 17 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3723955 | 3723955 | (2015 NK3) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3723955 | 21.30 | {'kilometers': {'estimated_diameter_min': 0.14... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 18 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3778647 | 3778647 | (2017 OR18) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3778647 | 21.30 | {'kilometers': {'estimated_diameter_min': 0.14... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 19 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3799743 | 3799743 | (2018 DT1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3799743 | 21.10 | {'kilometers': {'estimated_diameter_min': 0.16... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 20 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3826623 | 3826623 | (2018 PV7) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3826623 | 24.20 | {'kilometers': {'estimated_diameter_min': 0.03... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 21 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 3843359 | 3843359 | (2019 OH1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=3843359 | 22.61 | {'kilometers': {'estimated_diameter_min': 0.07... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 22 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54050972 | 54050972 | (2020 OC7) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54050972 | 20.80 | {'kilometers': {'estimated_diameter_min': 0.18... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 23 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54075323 | 54075323 | (2020 TN8) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54075323 | 20.60 | {'kilometers': {'estimated_diameter_min': 0.20... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 24 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54106530 | 54106530 | (2021 BP1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54106530 | 29.60 | {'kilometers': {'estimated_diameter_min': 0.00... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 25 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54247854 | 54247854 | (2016 FL60) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54247854 | 23.92 | {'kilometers': {'estimated_diameter_min': 0.04... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
| 26 | {'self': 'http://www.neowsapp.com/rest/v1/neo/... | 54248345 | 54248345 | (2022 DN1) | http://ssd.jpl.nasa.gov/sbdb.cgi?sstr=54248345 | 26.32 | {'kilometers': {'estimated_diameter_min': 0.01... | False | [{'close_approach_date': '2022-08-16', 'close_... | False |
Sweet! Now we have this data ready to do more analysis on which I will do something with in a future post :]
To get a forecast reading, we need to 3 things:
The base URL for a forecase reading looks as follows:
https://api.weather.gov/gridpoints/{office}/{grid X},{grid Y}/forecast
For this example, I'll show how to get the weather forecast for New York City. The latitude and longitude of NYC are 40.7128° N, 74.0060° W.
grid_X = '40.730610'
grid_Y = '-73.935242'
url = f'https://api.weather.gov/points/{grid_X},{grid_Y}'
rmd = requests.get(url)
rmd = rmd.json()
rmd
{'@context': ['https://geojson.org/geojson-ld/geojson-context.jsonld',
{'@version': '1.1',
'wx': 'https://api.weather.gov/ontology#',
's': 'https://schema.org/',
'geo': 'http://www.opengis.net/ont/geosparql#',
'unit': 'http://codes.wmo.int/common/unit/',
'@vocab': 'https://api.weather.gov/ontology#',
'geometry': {'@id': 's:GeoCoordinates', '@type': 'geo:wktLiteral'},
'city': 's:addressLocality',
'state': 's:addressRegion',
'distance': {'@id': 's:Distance', '@type': 's:QuantitativeValue'},
'bearing': {'@type': 's:QuantitativeValue'},
'value': {'@id': 's:value'},
'unitCode': {'@id': 's:unitCode', '@type': '@id'},
'forecastOffice': {'@type': '@id'},
'forecastGridData': {'@type': '@id'},
'publicZone': {'@type': '@id'},
'county': {'@type': '@id'}}],
'id': 'https://api.weather.gov/points/40.7306,-73.9352',
'type': 'Feature',
'geometry': {'type': 'Point', 'coordinates': [-73.9352, 40.7306]},
'properties': {'@id': 'https://api.weather.gov/points/40.7306,-73.9352',
'@type': 'wx:Point',
'cwa': 'OKX',
'forecastOffice': 'https://api.weather.gov/offices/OKX',
'gridId': 'OKX',
'gridX': 35,
'gridY': 35,
'forecast': 'https://api.weather.gov/gridpoints/OKX/35,35/forecast',
'forecastHourly': 'https://api.weather.gov/gridpoints/OKX/35,35/forecast/hourly',
'forecastGridData': 'https://api.weather.gov/gridpoints/OKX/35,35',
'observationStations': 'https://api.weather.gov/gridpoints/OKX/35,35/stations',
'relativeLocation': {'type': 'Feature',
'geometry': {'type': 'Point', 'coordinates': [-73.938697, 40.663468]},
'properties': {'city': 'New York',
'state': 'NY',
'distance': {'unitCode': 'wmoUnit:m', 'value': 7470.5677693295},
'bearing': {'unitCode': 'wmoUnit:degree_(angle)', 'value': 2}}},
'forecastZone': 'https://api.weather.gov/zones/forecast/NYZ176',
'county': 'https://api.weather.gov/zones/county/NYC081',
'fireWeatherZone': 'https://api.weather.gov/zones/fire/NYZ176',
'timeZone': 'America/New_York',
'radarStation': 'KOKX'}}
Now that we have the metadata for NYC we can get information about the forecast using the URL provided in the json. So let's do that.
url = 'https://api.weather.gov/gridpoints/OKX/35,35/forecast'
nyc_forecast_request = requests.get(url)
nyc_forecast_request
nyc_forecast = nyc_forecast_request.json()
nyc_forecast
{'@context': ['https://geojson.org/geojson-ld/geojson-context.jsonld',
{'@version': '1.1',
'wx': 'https://api.weather.gov/ontology#',
'geo': 'http://www.opengis.net/ont/geosparql#',
'unit': 'http://codes.wmo.int/common/unit/',
'@vocab': 'https://api.weather.gov/ontology#'}],
'type': 'Feature',
'geometry': {'type': 'Polygon',
'coordinates': [[[-73.9347941, 40.7385619],
[-73.9392746, 40.716883100000004],
[-73.9106703, 40.7134854],
[-73.9061839, 40.7351638],
[-73.9347941, 40.7385619]]]},
'properties': {'updated': '2022-08-18T21:44:54+00:00',
'units': 'us',
'forecastGenerator': 'BaselineForecastGenerator',
'generatedAt': '2022-08-19T02:55:16+00:00',
'updateTime': '2022-08-18T21:44:54+00:00',
'validTimes': '2022-08-18T15:00:00+00:00/P7DT22H',
'elevation': {'unitCode': 'wmoUnit:m', 'value': 14.9352},
'periods': [{'number': 1,
'name': 'Tonight',
'startTime': '2022-08-18T22:00:00-04:00',
'endTime': '2022-08-19T06:00:00-04:00',
'isDaytime': False,
'temperature': 73,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '3 to 7 mph',
'windDirection': 'W',
'icon': 'https://api.weather.gov/icons/land/night/few?size=medium',
'shortForecast': 'Mostly Clear',
'detailedForecast': 'Mostly clear, with a low around 73. West wind 3 to 7 mph.'},
{'number': 2,
'name': 'Friday',
'startTime': '2022-08-19T06:00:00-04:00',
'endTime': '2022-08-19T18:00:00-04:00',
'isDaytime': True,
'temperature': 89,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '5 to 12 mph',
'windDirection': 'SW',
'icon': 'https://api.weather.gov/icons/land/day/sct?size=medium',
'shortForecast': 'Mostly Sunny',
'detailedForecast': 'Mostly sunny, with a high near 89. Southwest wind 5 to 12 mph.'},
{'number': 3,
'name': 'Friday Night',
'startTime': '2022-08-19T18:00:00-04:00',
'endTime': '2022-08-20T06:00:00-04:00',
'isDaytime': False,
'temperature': 74,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '5 to 12 mph',
'windDirection': 'SW',
'icon': 'https://api.weather.gov/icons/land/night/sct?size=medium',
'shortForecast': 'Partly Cloudy',
'detailedForecast': 'Partly cloudy, with a low around 74. Southwest wind 5 to 12 mph.'},
{'number': 4,
'name': 'Saturday',
'startTime': '2022-08-20T06:00:00-04:00',
'endTime': '2022-08-20T18:00:00-04:00',
'isDaytime': True,
'temperature': 86,
'temperatureUnit': 'F',
'temperatureTrend': 'falling',
'windSpeed': '2 to 13 mph',
'windDirection': 'S',
'icon': 'https://api.weather.gov/icons/land/day/bkn?size=medium',
'shortForecast': 'Partly Sunny',
'detailedForecast': 'Partly sunny. High near 86, with temperatures falling to around 84 in the afternoon. South wind 2 to 13 mph.'},
{'number': 5,
'name': 'Saturday Night',
'startTime': '2022-08-20T18:00:00-04:00',
'endTime': '2022-08-21T06:00:00-04:00',
'isDaytime': False,
'temperature': 71,
'temperatureUnit': 'F',
'temperatureTrend': 'rising',
'windSpeed': '3 to 12 mph',
'windDirection': 'SE',
'icon': 'https://api.weather.gov/icons/land/night/sct?size=medium',
'shortForecast': 'Partly Cloudy',
'detailedForecast': 'Partly cloudy. Low around 71, with temperatures rising to around 73 overnight. Southeast wind 3 to 12 mph.'},
{'number': 6,
'name': 'Sunday',
'startTime': '2022-08-21T06:00:00-04:00',
'endTime': '2022-08-21T18:00:00-04:00',
'isDaytime': True,
'temperature': 85,
'temperatureUnit': 'F',
'temperatureTrend': 'falling',
'windSpeed': '3 to 12 mph',
'windDirection': 'SE',
'icon': 'https://api.weather.gov/icons/land/day/bkn/rain_showers?size=medium',
'shortForecast': 'Partly Sunny then Slight Chance Rain Showers',
'detailedForecast': 'A slight chance of rain showers after 2pm. Partly sunny. High near 85, with temperatures falling to around 81 in the afternoon.'},
{'number': 7,
'name': 'Sunday Night',
'startTime': '2022-08-21T18:00:00-04:00',
'endTime': '2022-08-22T06:00:00-04:00',
'isDaytime': False,
'temperature': 72,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '3 to 10 mph',
'windDirection': 'SE',
'icon': 'https://api.weather.gov/icons/land/night/rain_showers,30/rain_showers,40?size=medium',
'shortForecast': 'Chance Rain Showers',
'detailedForecast': 'A chance of rain showers. Mostly cloudy, with a low around 72. Chance of precipitation is 40%.'},
{'number': 8,
'name': 'Monday',
'startTime': '2022-08-22T06:00:00-04:00',
'endTime': '2022-08-22T18:00:00-04:00',
'isDaytime': True,
'temperature': 82,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '3 to 8 mph',
'windDirection': 'SE',
'icon': 'https://api.weather.gov/icons/land/day/rain_showers,40/tsra,60?size=medium',
'shortForecast': 'Showers And Thunderstorms Likely',
'detailedForecast': 'A chance of rain showers before 2pm, then showers and thunderstorms likely. Mostly cloudy, with a high near 82. Chance of precipitation is 60%.'},
{'number': 9,
'name': 'Monday Night',
'startTime': '2022-08-22T18:00:00-04:00',
'endTime': '2022-08-23T06:00:00-04:00',
'isDaytime': False,
'temperature': 71,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '2 to 7 mph',
'windDirection': 'SW',
'icon': 'https://api.weather.gov/icons/land/night/tsra_sct,60/tsra_sct,40?size=medium',
'shortForecast': 'Showers And Thunderstorms Likely',
'detailedForecast': 'Showers and thunderstorms likely. Mostly cloudy, with a low around 71. Chance of precipitation is 60%.'},
{'number': 10,
'name': 'Tuesday',
'startTime': '2022-08-23T06:00:00-04:00',
'endTime': '2022-08-23T18:00:00-04:00',
'isDaytime': True,
'temperature': 84,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '2 to 9 mph',
'windDirection': 'W',
'icon': 'https://api.weather.gov/icons/land/day/rain_showers,30/tsra_sct,40?size=medium',
'shortForecast': 'Chance Rain Showers',
'detailedForecast': 'A chance of rain showers before 2pm, then a chance of showers and thunderstorms. Partly sunny, with a high near 84. Chance of precipitation is 40%.'},
{'number': 11,
'name': 'Tuesday Night',
'startTime': '2022-08-23T18:00:00-04:00',
'endTime': '2022-08-24T06:00:00-04:00',
'isDaytime': False,
'temperature': 71,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '3 to 8 mph',
'windDirection': 'SW',
'icon': 'https://api.weather.gov/icons/land/night/tsra_hi,40/tsra_hi?size=medium',
'shortForecast': 'Chance Showers And Thunderstorms then Slight Chance Showers And Thunderstorms',
'detailedForecast': 'A chance of showers and thunderstorms before 8pm, then a slight chance of showers and thunderstorms between 8pm and 2am. Partly cloudy, with a low around 71. Chance of precipitation is 40%.'},
{'number': 12,
'name': 'Wednesday',
'startTime': '2022-08-24T06:00:00-04:00',
'endTime': '2022-08-24T18:00:00-04:00',
'isDaytime': True,
'temperature': 87,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '5 to 9 mph',
'windDirection': 'W',
'icon': 'https://api.weather.gov/icons/land/day/sct/rain_showers?size=medium',
'shortForecast': 'Mostly Sunny then Slight Chance Rain Showers',
'detailedForecast': 'A slight chance of rain showers after 2pm. Mostly sunny, with a high near 87.'},
{'number': 13,
'name': 'Wednesday Night',
'startTime': '2022-08-24T18:00:00-04:00',
'endTime': '2022-08-25T06:00:00-04:00',
'isDaytime': False,
'temperature': 72,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '3 to 9 mph',
'windDirection': 'SW',
'icon': 'https://api.weather.gov/icons/land/night/rain_showers/sct?size=medium',
'shortForecast': 'Slight Chance Rain Showers then Partly Cloudy',
'detailedForecast': 'A slight chance of rain showers before 8pm. Partly cloudy, with a low around 72.'},
{'number': 14,
'name': 'Thursday',
'startTime': '2022-08-25T06:00:00-04:00',
'endTime': '2022-08-25T18:00:00-04:00',
'isDaytime': True,
'temperature': 88,
'temperatureUnit': 'F',
'temperatureTrend': None,
'windSpeed': '3 to 10 mph',
'windDirection': 'W',
'icon': 'https://api.weather.gov/icons/land/day/sct?size=medium',
'shortForecast': 'Mostly Sunny',
'detailedForecast': 'Mostly sunny, with a high near 88.'}]}}
What do you know... another nested json dataset. No problem though! The data we want is within at the periods level so we just have to set that for our DataFrame entry and now we have our weekly forecast placed nicely in a pandas dataframe! :]
nyc_fc_df = pd.json_normalize(nyc_forecast['properties']['periods'])
nyc_fc_df
| number | name | startTime | endTime | isDaytime | temperature | temperatureUnit | temperatureTrend | windSpeed | windDirection | icon | shortForecast | detailedForecast | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Tonight | 2022-08-18T22:00:00-04:00 | 2022-08-19T06:00:00-04:00 | False | 73 | F | None | 3 to 7 mph | W | https://api.weather.gov/icons/land/night/few?s... | Mostly Clear | Mostly clear, with a low around 73. West wind ... |
| 1 | 2 | Friday | 2022-08-19T06:00:00-04:00 | 2022-08-19T18:00:00-04:00 | True | 89 | F | None | 5 to 12 mph | SW | https://api.weather.gov/icons/land/day/sct?siz... | Mostly Sunny | Mostly sunny, with a high near 89. Southwest w... |
| 2 | 3 | Friday Night | 2022-08-19T18:00:00-04:00 | 2022-08-20T06:00:00-04:00 | False | 74 | F | None | 5 to 12 mph | SW | https://api.weather.gov/icons/land/night/sct?s... | Partly Cloudy | Partly cloudy, with a low around 74. Southwest... |
| 3 | 4 | Saturday | 2022-08-20T06:00:00-04:00 | 2022-08-20T18:00:00-04:00 | True | 86 | F | falling | 2 to 13 mph | S | https://api.weather.gov/icons/land/day/bkn?siz... | Partly Sunny | Partly sunny. High near 86, with temperatures ... |
| 4 | 5 | Saturday Night | 2022-08-20T18:00:00-04:00 | 2022-08-21T06:00:00-04:00 | False | 71 | F | rising | 3 to 12 mph | SE | https://api.weather.gov/icons/land/night/sct?s... | Partly Cloudy | Partly cloudy. Low around 71, with temperature... |
| 5 | 6 | Sunday | 2022-08-21T06:00:00-04:00 | 2022-08-21T18:00:00-04:00 | True | 85 | F | falling | 3 to 12 mph | SE | https://api.weather.gov/icons/land/day/bkn/rai... | Partly Sunny then Slight Chance Rain Showers | A slight chance of rain showers after 2pm. Par... |
| 6 | 7 | Sunday Night | 2022-08-21T18:00:00-04:00 | 2022-08-22T06:00:00-04:00 | False | 72 | F | None | 3 to 10 mph | SE | https://api.weather.gov/icons/land/night/rain_... | Chance Rain Showers | A chance of rain showers. Mostly cloudy, with ... |
| 7 | 8 | Monday | 2022-08-22T06:00:00-04:00 | 2022-08-22T18:00:00-04:00 | True | 82 | F | None | 3 to 8 mph | SE | https://api.weather.gov/icons/land/day/rain_sh... | Showers And Thunderstorms Likely | A chance of rain showers before 2pm, then show... |
| 8 | 9 | Monday Night | 2022-08-22T18:00:00-04:00 | 2022-08-23T06:00:00-04:00 | False | 71 | F | None | 2 to 7 mph | SW | https://api.weather.gov/icons/land/night/tsra_... | Showers And Thunderstorms Likely | Showers and thunderstorms likely. Mostly cloud... |
| 9 | 10 | Tuesday | 2022-08-23T06:00:00-04:00 | 2022-08-23T18:00:00-04:00 | True | 84 | F | None | 2 to 9 mph | W | https://api.weather.gov/icons/land/day/rain_sh... | Chance Rain Showers | A chance of rain showers before 2pm, then a ch... |
| 10 | 11 | Tuesday Night | 2022-08-23T18:00:00-04:00 | 2022-08-24T06:00:00-04:00 | False | 71 | F | None | 3 to 8 mph | SW | https://api.weather.gov/icons/land/night/tsra_... | Chance Showers And Thunderstorms then Slight C... | A chance of showers and thunderstorms before 8... |
| 11 | 12 | Wednesday | 2022-08-24T06:00:00-04:00 | 2022-08-24T18:00:00-04:00 | True | 87 | F | None | 5 to 9 mph | W | https://api.weather.gov/icons/land/day/sct/rai... | Mostly Sunny then Slight Chance Rain Showers | A slight chance of rain showers after 2pm. Mos... |
| 12 | 13 | Wednesday Night | 2022-08-24T18:00:00-04:00 | 2022-08-25T06:00:00-04:00 | False | 72 | F | None | 3 to 9 mph | SW | https://api.weather.gov/icons/land/night/rain_... | Slight Chance Rain Showers then Partly Cloudy | A slight chance of rain showers before 8pm. Pa... |
| 13 | 14 | Thursday | 2022-08-25T06:00:00-04:00 | 2022-08-25T18:00:00-04:00 | True | 88 | F | None | 3 to 10 mph | W | https://api.weather.gov/icons/land/day/sct?siz... | Mostly Sunny | Mostly sunny, with a high near 88. |
The URL has the following basic structure:
https://earthquake.usgs.gov/fdsnws/event/1/[METHOD[?PARAMETERS]]
We'll be requesting the METHOD to be format=geojason in order to return a json file. There are options to get your data as a csv, xml, and plain text too though.
The parameters can return help refine our query by providing information regarding the time, desired location and many others for earthquake data.
Let's start by seeing what kind of information we can extract
url = 'https://earthquake.usgs.gov/fdsnws/event/1/application.json'
r = requests.get(url)
r = r.json()
r
{'catalogs': ['38457511',
'=c',
'aacse',
'ak',
'at',
'atlas',
'av',
'cdmg',
'cgs',
'choy',
'ci',
'cidev',
'dr',
'duputel',
'eqh',
'ev',
'ew',
'ew_dm',
'gcmt',
'gsc',
'hv',
'id',
'is',
'iscgem',
'iscgemsup',
'ismpkansas',
'ld',
'mb',
'nc',
'ne',
'nm',
'nn',
'official',
'official19631013051759_30',
'ok',
'ott',
'pags',
'pal',
'pde',
'pr',
'pt',
'rusms',
'sc',
'se',
'tx',
'us',
'usauto',
'ushis',
'uu',
'uw',
'wes'],
'contributors': ['admin',
'ak',
'at',
'atlas',
'av',
'cgs',
'ci',
'ew',
'hv',
'ismp',
'ld',
'mb',
'nc',
'nm',
'nn',
'np',
'official',
'ok',
'pr',
'prod01-pdl01.cr.usgs.gov',
'prod02-pdl01.cr.usgs.gov',
'pt',
'se',
'tx',
'us',
'uu',
'uw'],
'producttypes': ['associate',
'cap',
'deleted-text',
'disassociate',
'dyfi',
'earthquake-name',
'finite-fault',
'focal-mechanism',
'general-header',
'general-link',
'general-text',
'geoserve',
'ground-failure',
'heartbeat',
'image',
'impact-link',
'impact-text',
'internal-moment-tensor',
'internal-origin',
'losspager',
'losspager-admin',
'moment-tensor',
'moreinformation',
'nearby-cities',
'oaf',
'origin',
'p-wave-travel-times',
'phase-data',
'poster',
'scitech-link',
'scitech-text',
'shake-alert',
'shakemap',
'significance',
'tectonic-summary',
'touch',
'trump',
'trump-cap',
'trump-dyfi',
'trump-general-link',
'trump-general-text',
'trump-geoserve',
'trump-ground-failure',
'trump-impact-text',
'trump-losspager',
'trump-moment-tensor',
'trump-nearby-cities',
'trump-origin',
'trump-phase-data',
'trump-shakemap',
'trump-tectonic-summary',
'unassociated-amplitude'],
'eventtypes': ['accidental explosion',
'acoustic noise',
'acoustic_noise',
'anthropogenic_event',
'building collapse',
'chemical explosion',
'chemical_explosion',
'collapse',
'earthquake',
'eq',
'experimental explosion',
'explosion',
'ice quake',
'induced or triggered event',
'industrial explosion',
'landslide',
'meteor',
'meteorite',
'mine collapse',
'mine_collapse',
'mining explosion',
'mining_explosion',
'not reported',
'not_reported',
'nuclear explosion',
'nuclear_explosion',
'other',
'other event',
'other_event',
'quarry',
'quarry blast',
'quarry_blast',
'rock burst',
'Rock Slide',
'rockslide',
'rock_burst',
'snow_avalanche',
'sonic boom',
'sonicboom',
'sonic_boom',
'volcanic eruption',
'volcanic explosion'],
'magnitudetypes': ['2',
'4',
'fa',
'H',
'hn',
'lg',
'm',
'ma',
'mb',
'MbLg',
'mb_lg',
'mc',
'md',
'mdl',
'Me',
'mfa',
'mh',
'Mi',
'mint',
'ml',
'mlg',
'mlr',
'mlv',
'Ms',
'ms_20',
'Mt',
'mun',
'mw',
'mwb',
'mwc',
'mwp',
'mwr',
'mww',
'no',
'uk',
'Unknown']}
Interesting! There's stuff about volcanic events, nuclear explosions, rock slides and even meteorites! Let's find out how many requests we can make
url = 'https://earthquake.usgs.gov/fdsnws/event/1/count?format=geojson'
r = requests.get(url)
r = r.json()
r
{'count': 8868, 'maxAllowed': 20000}
start ='2014-01-01'
end = '2014-01-02'
url = f'https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime={start}&endtime={end}'
r = requests.get(url)
r = r.json()
r
{'type': 'FeatureCollection',
'metadata': {'generated': 1660880240000,
'url': 'https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2014-01-01&endtime=2014-01-02',
'title': 'USGS Earthquakes',
'status': 200,
'api': '1.13.6',
'count': 326},
'features': [{'type': 'Feature',
'properties': {'mag': 1.29,
'place': '10km SSW of Idyllwild, CA',
'time': 1388620296020,
'updated': 1457728844428,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ci11408890',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ci11408890&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 26,
'net': 'ci',
'code': '11408890',
'ids': ',ci11408890,',
'sources': ',ci,',
'types': ',cap,focal-mechanism,nearby-cities,origin,phase-data,scitech-link,',
'nst': 39,
'dmin': 0.06729,
'rms': 0.09,
'gap': 51,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 1.3 - 10km SSW of Idyllwild, CA'},
'geometry': {'type': 'Point',
'coordinates': [-116.7776667, 33.6633333, 11.008]},
'id': 'ci11408890'},
{'type': 'Feature',
'properties': {'mag': 1.1,
'place': 'Central Alaska',
'time': 1388620046501,
'updated': 1558392330681,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak01421ig3u',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak01421ig3u&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 19,
'net': 'ak',
'code': '01421ig3u',
'ids': ',ak10992887,ak01421ig3u,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.57,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 1.1 - Central Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-151.6459, 63.102, 14.1]},
'id': 'ak01421ig3u'},
{'type': 'Feature',
'properties': {'mag': 1.2,
'place': '7 km SSW of Big Lake, Alaska',
'time': 1388619956476,
'updated': 1558392330249,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak01421i2zj',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak01421i2zj&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 22,
'net': 'ak',
'code': '01421i2zj',
'ids': ',ak10934318,ak01421i2zj,',
'sources': ',ak,ak,',
'types': ',associate,cap,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.47,
'gap': None,
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
'properties': {'mag': 4.6,
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
'properties': {'mag': 4.6,
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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'sig': 5,
'net': 'nc',
'code': '72133856',
'ids': ',nc72133856,',
'sources': ',nc,',
'types': ',cap,nearby-cities,origin,phase-data,',
'nst': 9,
'dmin': 0.1423,
'rms': 0.08,
'gap': 126,
'magType': 'md',
'type': 'earthquake',
'title': 'M 0.6 - 6 km SSE of Mineral, California'},
'geometry': {'type': 'Point',
'coordinates': [-121.5533333, 40.2953333, 2.146]},
'id': 'nc72133856'},
{'type': 'Feature',
'properties': {'mag': 2.19,
'place': '23 km WSW of Malad City, Idaho',
'time': 1388539667160,
'updated': 1536775960820,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/uu60050572',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=uu60050572&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 74,
'net': 'uu',
'code': '60050572',
'ids': ',uu60050572,',
'sources': ',uu,',
'types': ',cap,origin,phase-data,',
'nst': 25,
'dmin': 0.02217,
'rms': 0.2,
'gap': 148,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 2.2 - 23 km WSW of Malad City, Idaho'},
'geometry': {'type': 'Point',
'coordinates': [-112.5171667, 42.1251667, 5.26]},
'id': 'uu60050572'},
{'type': 'Feature',
'properties': {'mag': 4.3,
'place': '68 km NNW of Pagudpud, Philippines',
'time': 1388539582990,
'updated': 1394151953000,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usb000m2ve',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=usb000m2ve&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 284,
'net': 'us',
'code': 'b000m2ve',
'ids': ',usb000m2ve,',
'sources': ',us,',
'types': ',origin,phase-data,',
'nst': None,
'dmin': 4.131,
'rms': 0.79,
'gap': 88,
'magType': 'mb',
'type': 'earthquake',
'title': 'M 4.3 - 68 km NNW of Pagudpud, Philippines'},
'geometry': {'type': 'Point', 'coordinates': [120.5772, 19.1486, 10]},
'id': 'usb000m2ve'},
{'type': 'Feature',
'properties': {'mag': 2.9,
'place': '134 km SSE of False Pass, Alaska',
'time': 1388539383150,
'updated': 1558392309582,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usc000lyb9',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=usc000lyb9&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 129,
'net': 'us',
'code': 'c000lyb9',
'ids': ',ak10992834,ak0141o9pt6,usc000lyb9,',
'sources': ',ak,ak,us,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.29,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 2.9 - 134 km SSE of False Pass, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-162.828, 53.695, 13.2]},
'id': 'usc000lyb9'},
{'type': 'Feature',
'properties': {'mag': 4.2,
'place': '215 km SSE of Kokopo, Papua New Guinea',
'time': 1388538507220,
'updated': 1394151953000,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usb000m2vd',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=usb000m2vd&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 271,
'net': 'us',
'code': 'b000m2vd',
'ids': ',usb000m2vd,',
'sources': ',us,',
'types': ',origin,phase-data,',
'nst': None,
'dmin': 6.443,
'rms': 0.8,
'gap': 141,
'magType': 'mb',
'type': 'earthquake',
'title': 'M 4.2 - 215 km SSE of Kokopo, Papua New Guinea'},
'geometry': {'type': 'Point', 'coordinates': [152.821, -6.2115, 40.81]},
'id': 'usb000m2vd'},
{'type': 'Feature',
'properties': {'mag': 0.58,
'place': '15km N of Borrego Springs, CA',
'time': 1388538268330,
'updated': 1457682051490,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ci11408442',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ci11408442&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 5,
'net': 'ci',
'code': '11408442',
'ids': ',ci11408442,',
'sources': ',ci,',
'types': ',cap,nearby-cities,origin,phase-data,scitech-link,',
'nst': 17,
'dmin': 0.1213,
'rms': 0.13,
'gap': 155,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 0.6 - 15km N of Borrego Springs, CA'},
'geometry': {'type': 'Point',
'coordinates': [-116.3898333, 33.3876667, 11.576]},
'id': 'ci11408442'},
{'type': 'Feature',
'properties': {'mag': 1.8,
'place': '42 km W of Salamatof, Alaska',
'time': 1388538057944,
'updated': 1558392308944,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak0141o4wki',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak0141o4wki&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 50,
'net': 'ak',
'code': '0141o4wki',
'ids': ',ak10928675,ak0141o4wki,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.39,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 1.8 - 42 km W of Salamatof, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-152.0889, 60.6054, 97.1]},
'id': 'ak0141o4wki'},
{'type': 'Feature',
'properties': {'mag': 2.3,
'place': '87 km SE of Nikolski, Alaska',
'time': 1388537642935,
'updated': 1558392308429,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak0141nuxdx',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak0141nuxdx&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 81,
'net': 'ak',
'code': '0141nuxdx',
'ids': ',ak10992832,ak0141nuxdx,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.21,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 2.3 - 87 km SE of Nikolski, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-167.878, 52.43, 14.8]},
'id': 'ak0141nuxdx'},
{'type': 'Feature',
'properties': {'mag': 0.1,
'place': '21km ESE of Anza, CA',
'time': 1388537639130,
'updated': 1457762408379,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ci11408434',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ci11408434&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 0,
'net': 'ci',
'code': '11408434',
'ids': ',ci11408434,',
'sources': ',ci,',
'types': ',cap,nearby-cities,origin,phase-data,scitech-link,',
'nst': 15,
'dmin': 0.1128,
'rms': 0.1,
'gap': 153,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 0.1 - 21km ESE of Anza, CA'},
'geometry': {'type': 'Point',
'coordinates': [-116.4728333, 33.4625, 12.559]},
'id': 'ci11408434'},
{'type': 'Feature',
'properties': {'mag': 1.3,
'place': '46 km ENE of Paxson, Alaska',
'time': 1388537221015,
'updated': 1558392307639,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak0141ntf85',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak0141ntf85&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 26,
'net': 'ak',
'code': '0141ntf85',
'ids': ',ak10992831,ak0141ntf85,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.57,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 1.3 - 46 km ENE of Paxson, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-144.7523, 63.236, 11.7]},
'id': 'ak0141ntf85'},
{'type': 'Feature',
'properties': {'mag': 0.22,
'place': 'Washington',
'time': 1388536764680,
'updated': 1469215315430,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/uw60664276',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=uw60664276&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 1,
'net': 'uw',
'code': '60664276',
'ids': ',uw60664276,',
'sources': ',uw,',
'types': ',cap,origin,phase-data,',
'nst': 9,
'dmin': 0.08565,
'rms': 0.1,
'gap': 182,
'magType': 'md',
'type': 'earthquake',
'title': 'M 0.2 - Washington'},
'geometry': {'type': 'Point',
'coordinates': [-122.5188333, 46.0926667, 13.97]},
'id': 'uw60664276'},
{'type': 'Feature',
'properties': {'mag': 1.07,
'place': '4 km WNW of Cobb, California',
'time': 1388536602470,
'updated': 1486062284045,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/nc72133846',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=nc72133846&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'automatic',
'tsunami': 0,
'sig': 18,
'net': 'nc',
'code': '72133846',
'ids': ',nc72133846,',
'sources': ',nc,',
'types': ',nearby-cities,origin,phase-data,',
'nst': 14,
'dmin': 0.006306,
'rms': 0.05,
'gap': 90,
'magType': 'md',
'type': 'earthquake',
'title': 'M 1.1 - 4 km WNW of Cobb, California'},
'geometry': {'type': 'Point',
'coordinates': [-122.7776642, 38.8320007, 0.187]},
'id': 'nc72133846'},
{'type': 'Feature',
'properties': {'mag': 2.08,
'place': '18 km SE of Fern Forest, Hawaii',
'time': 1388536590280,
'updated': 1594067950160,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/hv60614021',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=hv60614021&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 67,
'net': 'hv',
'code': '60614021',
'ids': ',hv60614021,',
'sources': ',hv,',
'types': ',cap,origin,phase-data,',
'nst': 38,
'dmin': None,
'rms': 0.14,
'gap': 207,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 2.1 - 18 km SE of Fern Forest, Hawaii'},
'geometry': {'type': 'Point',
'coordinates': [-155.0353333, 19.3246667, 1.294]},
'id': 'hv60614021'},
{'type': 'Feature',
'properties': {'mag': 2,
'place': '69 km ENE of Pedro Bay, Alaska',
'time': 1388535883771,
'updated': 1558392307098,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak0141nol21',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak0141nol21&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 62,
'net': 'ak',
'code': '0141nol21',
'ids': ',ak10992830,ak0141nol21,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.77,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 2.0 - 69 km ENE of Pedro Bay, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-153.0185, 60.0872, 105.8]},
'id': 'ak0141nol21'},
{'type': 'Feature',
'properties': {'mag': 0.57,
'place': '10 km W of Cobb, California',
'time': 1388535010970,
'updated': 1486062277926,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/nc72133841',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=nc72133841&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 5,
'net': 'nc',
'code': '72133841',
'ids': ',nc72133841,',
'sources': ',nc,',
'types': ',nearby-cities,origin,phase-data,scitech-link,',
'nst': 30,
'dmin': 0.007207,
'rms': 0.04,
'gap': 65,
'magType': 'md',
'type': 'earthquake',
'title': 'M 0.6 - 10 km W of Cobb, California'},
'geometry': {'type': 'Point', 'coordinates': [-122.8435, 38.8216667, 2.14]},
'id': 'nc72133841'},
{'type': 'Feature',
'properties': {'mag': 2.2,
'place': '1 km NW of Sweet Home, Oregon',
'time': 1388534755060,
'updated': 1469215315190,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/uw60664271',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=uw60664271&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 74,
'net': 'uw',
'code': '60664271',
'ids': ',uw60664271,',
'sources': ',uw,',
'types': ',cap,origin,phase-data,',
'nst': 4,
'dmin': 0.1157,
'rms': 0.07,
'gap': 179,
'magType': 'md',
'type': 'explosion',
'title': 'M 2.2 Explosion - 1 km NW of Sweet Home, Oregon'},
'geometry': {'type': 'Point',
'coordinates': [-122.7498333, 44.408, -0.348]},
'id': 'uw60664271'},
{'type': 'Feature',
'properties': {'mag': 4.5,
'place': '163 km W of Neiafu, Tonga',
'time': 1388534728830,
'updated': 1394151953000,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usc000lv5j',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=usc000lv5j&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 312,
'net': 'us',
'code': 'c000lv5j',
'ids': ',usc000lv5j,',
'sources': ',us,',
'types': ',cap,origin,phase-data,',
'nst': None,
'dmin': 5.337,
'rms': 0.6,
'gap': 41,
'magType': 'mb',
'type': 'earthquake',
'title': 'M 4.5 - 163 km W of Neiafu, Tonga'},
'geometry': {'type': 'Point', 'coordinates': [-175.5282, -18.4965, 217.9]},
'id': 'usc000lv5j'},
{'type': 'Feature',
'properties': {'mag': 5.1,
'place': '76 km NNW of Davila, Philippines',
'time': 1388534476610,
'updated': 1394151953000,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usc000lv5e',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=usc000lv5e&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 400,
'net': 'us',
'code': 'c000lv5e',
'ids': ',usc000lv5e,',
'sources': ',us,',
'types': ',cap,origin,phase-data,',
'nst': None,
'dmin': 3.794,
'rms': 0.85,
'gap': 29,
'magType': 'mb',
'type': 'earthquake',
'title': 'M 5.1 - 76 km NNW of Davila, Philippines'},
'geometry': {'type': 'Point', 'coordinates': [120.2389, 19.0868, 10.07]},
'id': 'usc000lv5e'}],
'bbox': [-179.688, -56.1218, -2.884, 167.8106, 64.8102, 582.05]}
We have data! Let's get this into a dataframe!
r['features']
[{'type': 'Feature',
'properties': {'mag': 1.29,
'place': '10km SSW of Idyllwild, CA',
'time': 1388620296020,
'updated': 1457728844428,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ci11408890',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ci11408890&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 26,
'net': 'ci',
'code': '11408890',
'ids': ',ci11408890,',
'sources': ',ci,',
'types': ',cap,focal-mechanism,nearby-cities,origin,phase-data,scitech-link,',
'nst': 39,
'dmin': 0.06729,
'rms': 0.09,
'gap': 51,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 1.3 - 10km SSW of Idyllwild, CA'},
'geometry': {'type': 'Point',
'coordinates': [-116.7776667, 33.6633333, 11.008]},
'id': 'ci11408890'},
{'type': 'Feature',
'properties': {'mag': 1.1,
'place': 'Central Alaska',
'time': 1388620046501,
'updated': 1558392330681,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak01421ig3u',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak01421ig3u&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 19,
'net': 'ak',
'code': '01421ig3u',
'ids': ',ak10992887,ak01421ig3u,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.57,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 1.1 - Central Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-151.6459, 63.102, 14.1]},
'id': 'ak01421ig3u'},
{'type': 'Feature',
'properties': {'mag': 1.2,
'place': '7 km SSW of Big Lake, Alaska',
'time': 1388619956476,
'updated': 1558392330249,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak01421i2zj',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak01421i2zj&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 22,
'net': 'ak',
'code': '01421i2zj',
'ids': ',ak10934318,ak01421i2zj,',
'sources': ',ak,ak,',
'types': ',associate,cap,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.47,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 1.2 - 7 km SSW of Big Lake, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-150.0165, 61.4581, 44.6]},
'id': 'ak01421i2zj'},
{'type': 'Feature',
'properties': {'mag': 1.4,
'place': '32 km N of Petersville, Alaska',
'time': 1388619763623,
'updated': 1558392329762,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak01421heui',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak01421heui&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 30,
'net': 'ak',
'code': '01421heui',
'ids': ',ak10992885,ak01421heui,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.35,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 1.4 - 32 km N of Petersville, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-150.8276, 62.7884, 87.6]},
'id': 'ak01421heui'},
{'type': 'Feature',
'properties': {'mag': 4,
'place': '28 km WNW of Coquimbo, Chile',
'time': 1388619735000,
'updated': 1394151954000,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usc000mnnn',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
'properties': {'mag': 4.9,
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
'properties': {'mag': 4.1,
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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{'type': 'Feature',
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'updated': 1394151953000,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usb000m2ve',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=usb000m2ve&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 284,
'net': 'us',
'code': 'b000m2ve',
'ids': ',usb000m2ve,',
'sources': ',us,',
'types': ',origin,phase-data,',
'nst': None,
'dmin': 4.131,
'rms': 0.79,
'gap': 88,
'magType': 'mb',
'type': 'earthquake',
'title': 'M 4.3 - 68 km NNW of Pagudpud, Philippines'},
'geometry': {'type': 'Point', 'coordinates': [120.5772, 19.1486, 10]},
'id': 'usb000m2ve'},
{'type': 'Feature',
'properties': {'mag': 2.9,
'place': '134 km SSE of False Pass, Alaska',
'time': 1388539383150,
'updated': 1558392309582,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usc000lyb9',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=usc000lyb9&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 129,
'net': 'us',
'code': 'c000lyb9',
'ids': ',ak10992834,ak0141o9pt6,usc000lyb9,',
'sources': ',ak,ak,us,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.29,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 2.9 - 134 km SSE of False Pass, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-162.828, 53.695, 13.2]},
'id': 'usc000lyb9'},
{'type': 'Feature',
'properties': {'mag': 4.2,
'place': '215 km SSE of Kokopo, Papua New Guinea',
'time': 1388538507220,
'updated': 1394151953000,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usb000m2vd',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=usb000m2vd&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 271,
'net': 'us',
'code': 'b000m2vd',
'ids': ',usb000m2vd,',
'sources': ',us,',
'types': ',origin,phase-data,',
'nst': None,
'dmin': 6.443,
'rms': 0.8,
'gap': 141,
'magType': 'mb',
'type': 'earthquake',
'title': 'M 4.2 - 215 km SSE of Kokopo, Papua New Guinea'},
'geometry': {'type': 'Point', 'coordinates': [152.821, -6.2115, 40.81]},
'id': 'usb000m2vd'},
{'type': 'Feature',
'properties': {'mag': 0.58,
'place': '15km N of Borrego Springs, CA',
'time': 1388538268330,
'updated': 1457682051490,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ci11408442',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ci11408442&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 5,
'net': 'ci',
'code': '11408442',
'ids': ',ci11408442,',
'sources': ',ci,',
'types': ',cap,nearby-cities,origin,phase-data,scitech-link,',
'nst': 17,
'dmin': 0.1213,
'rms': 0.13,
'gap': 155,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 0.6 - 15km N of Borrego Springs, CA'},
'geometry': {'type': 'Point',
'coordinates': [-116.3898333, 33.3876667, 11.576]},
'id': 'ci11408442'},
{'type': 'Feature',
'properties': {'mag': 1.8,
'place': '42 km W of Salamatof, Alaska',
'time': 1388538057944,
'updated': 1558392308944,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak0141o4wki',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak0141o4wki&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 50,
'net': 'ak',
'code': '0141o4wki',
'ids': ',ak10928675,ak0141o4wki,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.39,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 1.8 - 42 km W of Salamatof, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-152.0889, 60.6054, 97.1]},
'id': 'ak0141o4wki'},
{'type': 'Feature',
'properties': {'mag': 2.3,
'place': '87 km SE of Nikolski, Alaska',
'time': 1388537642935,
'updated': 1558392308429,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak0141nuxdx',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak0141nuxdx&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 81,
'net': 'ak',
'code': '0141nuxdx',
'ids': ',ak10992832,ak0141nuxdx,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.21,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 2.3 - 87 km SE of Nikolski, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-167.878, 52.43, 14.8]},
'id': 'ak0141nuxdx'},
{'type': 'Feature',
'properties': {'mag': 0.1,
'place': '21km ESE of Anza, CA',
'time': 1388537639130,
'updated': 1457762408379,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ci11408434',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ci11408434&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 0,
'net': 'ci',
'code': '11408434',
'ids': ',ci11408434,',
'sources': ',ci,',
'types': ',cap,nearby-cities,origin,phase-data,scitech-link,',
'nst': 15,
'dmin': 0.1128,
'rms': 0.1,
'gap': 153,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 0.1 - 21km ESE of Anza, CA'},
'geometry': {'type': 'Point',
'coordinates': [-116.4728333, 33.4625, 12.559]},
'id': 'ci11408434'},
{'type': 'Feature',
'properties': {'mag': 1.3,
'place': '46 km ENE of Paxson, Alaska',
'time': 1388537221015,
'updated': 1558392307639,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak0141ntf85',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak0141ntf85&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 26,
'net': 'ak',
'code': '0141ntf85',
'ids': ',ak10992831,ak0141ntf85,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.57,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 1.3 - 46 km ENE of Paxson, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-144.7523, 63.236, 11.7]},
'id': 'ak0141ntf85'},
{'type': 'Feature',
'properties': {'mag': 0.22,
'place': 'Washington',
'time': 1388536764680,
'updated': 1469215315430,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/uw60664276',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=uw60664276&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 1,
'net': 'uw',
'code': '60664276',
'ids': ',uw60664276,',
'sources': ',uw,',
'types': ',cap,origin,phase-data,',
'nst': 9,
'dmin': 0.08565,
'rms': 0.1,
'gap': 182,
'magType': 'md',
'type': 'earthquake',
'title': 'M 0.2 - Washington'},
'geometry': {'type': 'Point',
'coordinates': [-122.5188333, 46.0926667, 13.97]},
'id': 'uw60664276'},
{'type': 'Feature',
'properties': {'mag': 1.07,
'place': '4 km WNW of Cobb, California',
'time': 1388536602470,
'updated': 1486062284045,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/nc72133846',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=nc72133846&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'automatic',
'tsunami': 0,
'sig': 18,
'net': 'nc',
'code': '72133846',
'ids': ',nc72133846,',
'sources': ',nc,',
'types': ',nearby-cities,origin,phase-data,',
'nst': 14,
'dmin': 0.006306,
'rms': 0.05,
'gap': 90,
'magType': 'md',
'type': 'earthquake',
'title': 'M 1.1 - 4 km WNW of Cobb, California'},
'geometry': {'type': 'Point',
'coordinates': [-122.7776642, 38.8320007, 0.187]},
'id': 'nc72133846'},
{'type': 'Feature',
'properties': {'mag': 2.08,
'place': '18 km SE of Fern Forest, Hawaii',
'time': 1388536590280,
'updated': 1594067950160,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/hv60614021',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=hv60614021&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 67,
'net': 'hv',
'code': '60614021',
'ids': ',hv60614021,',
'sources': ',hv,',
'types': ',cap,origin,phase-data,',
'nst': 38,
'dmin': None,
'rms': 0.14,
'gap': 207,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 2.1 - 18 km SE of Fern Forest, Hawaii'},
'geometry': {'type': 'Point',
'coordinates': [-155.0353333, 19.3246667, 1.294]},
'id': 'hv60614021'},
{'type': 'Feature',
'properties': {'mag': 2,
'place': '69 km ENE of Pedro Bay, Alaska',
'time': 1388535883771,
'updated': 1558392307098,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/ak0141nol21',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=ak0141nol21&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 62,
'net': 'ak',
'code': '0141nol21',
'ids': ',ak10992830,ak0141nol21,',
'sources': ',ak,ak,',
'types': ',associate,origin,phase-data,',
'nst': None,
'dmin': None,
'rms': 0.77,
'gap': None,
'magType': 'ml',
'type': 'earthquake',
'title': 'M 2.0 - 69 km ENE of Pedro Bay, Alaska'},
'geometry': {'type': 'Point', 'coordinates': [-153.0185, 60.0872, 105.8]},
'id': 'ak0141nol21'},
{'type': 'Feature',
'properties': {'mag': 0.57,
'place': '10 km W of Cobb, California',
'time': 1388535010970,
'updated': 1486062277926,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/nc72133841',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=nc72133841&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 5,
'net': 'nc',
'code': '72133841',
'ids': ',nc72133841,',
'sources': ',nc,',
'types': ',nearby-cities,origin,phase-data,scitech-link,',
'nst': 30,
'dmin': 0.007207,
'rms': 0.04,
'gap': 65,
'magType': 'md',
'type': 'earthquake',
'title': 'M 0.6 - 10 km W of Cobb, California'},
'geometry': {'type': 'Point', 'coordinates': [-122.8435, 38.8216667, 2.14]},
'id': 'nc72133841'},
{'type': 'Feature',
'properties': {'mag': 2.2,
'place': '1 km NW of Sweet Home, Oregon',
'time': 1388534755060,
'updated': 1469215315190,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/uw60664271',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=uw60664271&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 74,
'net': 'uw',
'code': '60664271',
'ids': ',uw60664271,',
'sources': ',uw,',
'types': ',cap,origin,phase-data,',
'nst': 4,
'dmin': 0.1157,
'rms': 0.07,
'gap': 179,
'magType': 'md',
'type': 'explosion',
'title': 'M 2.2 Explosion - 1 km NW of Sweet Home, Oregon'},
'geometry': {'type': 'Point', 'coordinates': [-122.7498333, 44.408, -0.348]},
'id': 'uw60664271'},
{'type': 'Feature',
'properties': {'mag': 4.5,
'place': '163 km W of Neiafu, Tonga',
'time': 1388534728830,
'updated': 1394151953000,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usc000lv5j',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=usc000lv5j&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 312,
'net': 'us',
'code': 'c000lv5j',
'ids': ',usc000lv5j,',
'sources': ',us,',
'types': ',cap,origin,phase-data,',
'nst': None,
'dmin': 5.337,
'rms': 0.6,
'gap': 41,
'magType': 'mb',
'type': 'earthquake',
'title': 'M 4.5 - 163 km W of Neiafu, Tonga'},
'geometry': {'type': 'Point', 'coordinates': [-175.5282, -18.4965, 217.9]},
'id': 'usc000lv5j'},
{'type': 'Feature',
'properties': {'mag': 5.1,
'place': '76 km NNW of Davila, Philippines',
'time': 1388534476610,
'updated': 1394151953000,
'tz': None,
'url': 'https://earthquake.usgs.gov/earthquakes/eventpage/usc000lv5e',
'detail': 'https://earthquake.usgs.gov/fdsnws/event/1/query?eventid=usc000lv5e&format=geojson',
'felt': None,
'cdi': None,
'mmi': None,
'alert': None,
'status': 'reviewed',
'tsunami': 0,
'sig': 400,
'net': 'us',
'code': 'c000lv5e',
'ids': ',usc000lv5e,',
'sources': ',us,',
'types': ',cap,origin,phase-data,',
'nst': None,
'dmin': 3.794,
'rms': 0.85,
'gap': 29,
'magType': 'mb',
'type': 'earthquake',
'title': 'M 5.1 - 76 km NNW of Davila, Philippines'},
'geometry': {'type': 'Point', 'coordinates': [120.2389, 19.0868, 10.07]},
'id': 'usc000lv5e'}]
earthquake_df = pd.json_normalize(r['features'])
earthquake_df
| type | id | properties.mag | properties.place | properties.time | properties.updated | properties.tz | properties.url | properties.detail | properties.felt | ... | properties.types | properties.nst | properties.dmin | properties.rms | properties.gap | properties.magType | properties.type | properties.title | geometry.type | geometry.coordinates | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Feature | ci11408890 | 1.29 | 10km SSW of Idyllwild, CA | 1388620296020 | 1457728844428 | None | https://earthquake.usgs.gov/earthquakes/eventp... | https://earthquake.usgs.gov/fdsnws/event/1/que... | NaN | ... | ,cap,focal-mechanism,nearby-cities,origin,phas... | 39.0 | 0.067290 | 0.09 | 51.0 | ml | earthquake | M 1.3 - 10km SSW of Idyllwild, CA | Point | [-116.7776667, 33.6633333, 11.008] |
| 1 | Feature | ak01421ig3u | 1.10 | Central Alaska | 1388620046501 | 1558392330681 | None | https://earthquake.usgs.gov/earthquakes/eventp... | https://earthquake.usgs.gov/fdsnws/event/1/que... | NaN | ... | ,associate,origin,phase-data, | NaN | NaN | 0.57 | NaN | ml | earthquake | M 1.1 - Central Alaska | Point | [-151.6459, 63.102, 14.1] |
| 2 | Feature | ak01421i2zj | 1.20 | 7 km SSW of Big Lake, Alaska | 1388619956476 | 1558392330249 | None | https://earthquake.usgs.gov/earthquakes/eventp... | https://earthquake.usgs.gov/fdsnws/event/1/que... | NaN | ... | ,associate,cap,origin,phase-data, | NaN | NaN | 0.47 | NaN | ml | earthquake | M 1.2 - 7 km SSW of Big Lake, Alaska | Point | [-150.0165, 61.4581, 44.6] |
| 3 | Feature | ak01421heui | 1.40 | 32 km N of Petersville, Alaska | 1388619763623 | 1558392329762 | None | https://earthquake.usgs.gov/earthquakes/eventp... | https://earthquake.usgs.gov/fdsnws/event/1/que... | NaN | ... | ,associate,origin,phase-data, | NaN | NaN | 0.35 | NaN | ml | earthquake | M 1.4 - 32 km N of Petersville, Alaska | Point | [-150.8276, 62.7884, 87.6] |
| 4 | Feature | usc000mnnn | 4.00 | 28 km WNW of Coquimbo, Chile | 1388619735000 | 1394151954000 | None | https://earthquake.usgs.gov/earthquakes/eventp... | https://earthquake.usgs.gov/fdsnws/event/1/que... | NaN | ... | ,origin,phase-data, | NaN | NaN | 3.09 | NaN | ml | earthquake | M 4.0 - 28 km WNW of Coquimbo, Chile | Point | [-71.621, -29.888, 40] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 321 | Feature | ak0141nol21 | 2.00 | 69 km ENE of Pedro Bay, Alaska | 1388535883771 | 1558392307098 | None | https://earthquake.usgs.gov/earthquakes/eventp... | https://earthquake.usgs.gov/fdsnws/event/1/que... | NaN | ... | ,associate,origin,phase-data, | NaN | NaN | 0.77 | NaN | ml | earthquake | M 2.0 - 69 km ENE of Pedro Bay, Alaska | Point | [-153.0185, 60.0872, 105.8] |
| 322 | Feature | nc72133841 | 0.57 | 10 km W of Cobb, California | 1388535010970 | 1486062277926 | None | https://earthquake.usgs.gov/earthquakes/eventp... | https://earthquake.usgs.gov/fdsnws/event/1/que... | NaN | ... | ,nearby-cities,origin,phase-data,scitech-link, | 30.0 | 0.007207 | 0.04 | 65.0 | md | earthquake | M 0.6 - 10 km W of Cobb, California | Point | [-122.8435, 38.8216667, 2.14] |
| 323 | Feature | uw60664271 | 2.20 | 1 km NW of Sweet Home, Oregon | 1388534755060 | 1469215315190 | None | https://earthquake.usgs.gov/earthquakes/eventp... | https://earthquake.usgs.gov/fdsnws/event/1/que... | NaN | ... | ,cap,origin,phase-data, | 4.0 | 0.115700 | 0.07 | 179.0 | md | explosion | M 2.2 Explosion - 1 km NW of Sweet Home, Oregon | Point | [-122.7498333, 44.408, -0.348] |
| 324 | Feature | usc000lv5j | 4.50 | 163 km W of Neiafu, Tonga | 1388534728830 | 1394151953000 | None | https://earthquake.usgs.gov/earthquakes/eventp... | https://earthquake.usgs.gov/fdsnws/event/1/que... | NaN | ... | ,cap,origin,phase-data, | NaN | 5.337000 | 0.60 | 41.0 | mb | earthquake | M 4.5 - 163 km W of Neiafu, Tonga | Point | [-175.5282, -18.4965, 217.9] |
| 325 | Feature | usc000lv5e | 5.10 | 76 km NNW of Davila, Philippines | 1388534476610 | 1394151953000 | None | https://earthquake.usgs.gov/earthquakes/eventp... | https://earthquake.usgs.gov/fdsnws/event/1/que... | NaN | ... | ,cap,origin,phase-data, | NaN | 3.794000 | 0.85 | 29.0 | mb | earthquake | M 5.1 - 76 km NNW of Davila, Philippines | Point | [120.2389, 19.0868, 10.07] |
326 rows × 30 columns
Awesome! More data to play with! Below is an example of how to run a query that has more specifications and that template could be used for even further refinments by adding more sections to the parameters string.
def earthquake(f):
parameters = {
"format": "geojson",
"starttime": "2020- 01-01",
"endtime": "2020-12-31",
"alertlevel": "orange"
}
data = requests.get(f, params = parameters)
data = json.loads(data.text)
return data
f = r"https://earthquake.usgs.gov/fdsnws/event/1/query?"
a = earthquake(f)
Here I'll show how to get different types of information like archives and movie reviews. You can get detailed information about the API usage here: https://developer.nytimes.com/apis
Let's start by getting the archive for the month of June and the year 2020.
year = '2020'
month = '6'
api_key = 'jhkMYAlxMsPPYrLatKFLwF6JbQLhME9V'
url = f'https://api.nytimes.com/svc/archive/v1/{year}/{month}.json?api-key={api_key}'
r = requests.get(url)
r = r.json()
r
{'copyright': 'Copyright (c) 2020 The New York Times Company. All Rights Reserved.',
'response': {'docs': [{'abstract': 'So far, 47 police vehicles have been damaged. Some protesters say the attacks reflect pent-up rage against the police, while officials have denounced the destruction.',
'web_url': 'https://www.nytimes.com/2020/05/31/nyregion/police-cars-nyc-protests.html',
'snippet': 'So far, 47 police vehicles have been damaged. Some protesters say the attacks reflect pent-up rage against the police, while officials have denounced the destruction.',
'lead_paragraph': 'On Friday night in the Fort Greene section of Brooklyn, protesters descended on an unoccupied New York Police Department van, which quickly became the target of all their anger and angst.',
'source': 'The New York Times',
'multimedia': [{'rank': 0,
'subtype': 'xlarge',
'caption': None,
'credit': None,
'type': 'image',
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{'abstract': 'The authorities said no one had been struck, but protesters reported that several people may have been injured.',
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{'abstract': 'Axe and Chuck grapple with the sins of their fathers. Wendy takes a deeper in interest in art.',
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'lead_paragraph': '“Old man, take a look at my life — I’m a lot like you.” This week’s episode of “Billions” uses Neil Young’s song “Old Man” to tie a bow around two of its story lines. In the first, Charles Rhoades Sr. has kidney failure, and his advanced age (and bad habits) makes him an unlikely candidate for a donor. It’s a predicament even his son Chuck’s legal wizardry may be unable to remedy.',
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{'abstract': 'Get help with parenting issues, blast a favorite show tune and, if you dare, sign up for an open mic.',
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'lead_paragraph': 'Here are a few of events happening this week and how to tune in (all times are Eastern).',
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'headline': {'main': 'Kick Off a Week of Community and Culture',
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'headline': {'main': 'Global Anger Grows Over George Floyd Death, and Becomes an Anti-Trump Cudgel',
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{'abstract': 'In “A Bit of a Stretch,” Chris Atkins writes about the nine months he spent at Wandsworth, one of the largest, oldest and unruliest prisons in Britain.',
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{'abstract': 'The attorney general has long held an expansive view of presidential power. With multiple crises converging in the run-up to the 2020 election, he is busy putting his theories to work.',
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{'abstract': 'After more than a decade of scandal, the sport can reintroduce itself to America by focusing on human drama and emotion.',
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'lead_paragraph': 'Baseball fans, it won’t be long. Despite the pandemic, the sport has too much to lose to not come back this year, and I predict that games will start again by the Fourth of July. That dramatic timing, with so many hungering for distraction after months of dystopian-novel headlines, could offer baseball a much-needed chance at relevance.',
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'headline': {'main': 'How Minneapolis, One of America’s Most Liberal Cities, Struggles With Racism',
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{'abstract': 'In search of creative inspiration, a New York-based photographer spent two weeks documenting Bangkok’s fresh markets and street vendors.',
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'lead_paragraph': 'With travel restrictions in place worldwide, we’ve launched a new series, The World Through a Lens, in which photojournalists help transport you, virtually, to some of our planet’s most beautiful and intriguing places. This week, Louise Palmberg shares a collection of photographs from the markets and food stands in Bangkok.',
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'lead_paragraph': 'This article is part of the On Tech newsletter. You can sign up here to receive it weekdays.',
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{'abstract': 'After weeks of virtual events, Mr. Biden went to Bethel A.M.E. Church in Wilmington to hear from community leaders.',
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{'abstract': 'Missing parties and parades during Pride Month? These movies will fill your screen with joy, history and rainbows.',
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{'abstract': 'The rapper earned his first No. 1 with “Wunna,” and now all eyes are on Lady Gaga, whose “Chromatica” is expected to take the top spot next week.',
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{'abstract': 'Prime Minister Benjamin Netanyahu has promised to annex much of the occupied West Bank. But now that he can, there are questions about whether he will.',
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'headline': {'main': 'Netanyahu’s Annexation Plans Meet Surprise Opponent: Israeli Settlers',
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{'abstract': 'Readers call on white Americans to “wake up,” question the president’s credibility on police brutality, encourage “taking a knee” in peaceful protest, and more.',
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{'abstract': 'His political program and ratings derailed by the coronavirus, President Vladimir Putin of Russia is pushing to get back on track.',
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'headline': {'main': 'Hong Kong Bans Tiananmen Vigil for 1st Time, in New Challenge to Protests',
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'pub_date': '2020-06-01T19:50:51+0000',
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{'abstract': 'These esteemed black American creators make work that responds to violence against the black body.',
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'snippet': 'These esteemed black American creators make work that responds to violence against the black body.',
'lead_paragraph': 'Yet another instance of searing racial injustice in this country, the death of George Floyd has galvanized people far and wide to demand change. Their efforts are a reminder that protest and consciousness-raising come in many forms. Sometimes, it is aesthetic in nature: “The power of the art of public engagement is in how, after news cycles and public outrage have moved on, it makes us see the things we don’t want to,” T’s writer at large Megan O’Grady writes in her 2018 piece on the performance artist Pope.L, best known for his physically punishing crawls through city streets. He, along with Nick Cave, Carrie Mae Weems, Arthur Jafa and the fashion designer Kerby Jean-Raymond, is among the contemporary artists who make work that addresses violence against the black body, presenting their audiences with reflections and refractions of our shared past and present. Here are profiles of each one of them, along with pictures of their powerful work.',
'source': 'The New York Times',
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'headline': {'main': 'Five Artists for This Moment',
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{'abstract': 'Lana Del Rey, Doja Cat, and how some in the limelight bristle at social media condemnation.',
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'lead_paragraph': 'In the last two weeks, the pop stars Lana Del Rey and Doja Cat have both found themselves in hot water on the internet over kerfuffles of their own creation. This is what life online is often like for the famous and demi-famous. Misstep, apologize, recalibrate, repeat.',
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{'abstract': 'Created as a family meal dish for the El Bulli staff a decade ago, this three-ingredient dish utilizes pantry staples.',
'web_url': 'https://www.nytimes.com/2020/06/01/dining/ferran-adria-potato-chip-omelet-recipe.html',
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'lead_paragraph': 'Even if you haven’t worked in a restaurant, the concept of family meal may be familiar: It’s the act of cobbling together a meal that salvages or repurposes ingredients to resourcefully feed a restaurant’s staff before service, and, ideally, to connect them at the table. The frugality of this kind of meal can be thrilling — it’s a marriage of hospitality and practicality — and it exemplifies how many Americans are preparing food right now, as many home cooks have leaned into making focaccia, growing victory gardens and stretching staple ingredients.',
'print_section': 'D',
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'source': 'The New York Times',
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'headline': {'main': 'Ferran Adrià’s Potato Chip Omelet Delights Beyond Expectation',
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{'name': 'subject', 'value': 'Potato Chips', 'rank': 4, 'major': 'N'},
{'name': 'subject', 'value': 'Olive Oil', 'rank': 5, 'major': 'N'}],
'pub_date': '2020-06-01T19:57:36+0000',
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'byline': {'original': 'By Alexa Weibel',
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{'abstract': 'Many artists have not been paid since March at the company, which hopes to return on New Year’s Eve after its longest interruption in over a century.',
'web_url': 'https://www.nytimes.com/2020/06/01/arts/music/metropolitan-opera-cancels-season-virus.html',
'snippet': 'Many artists have not been paid since March at the company, which hopes to return on New Year’s Eve after its longest interruption in over a century.',
'lead_paragraph': 'The Metropolitan Opera said on Monday that the coronavirus pandemic would force the company to cancel its fall season, thrusting the Met into one of the gravest crises in its 137-year history and leaving many of its artists, who have not been paid since March, in dire financial straits.',
'print_section': 'A',
'print_page': '4',
'source': 'The New York Times',
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'headline': {'main': 'The Metropolitan Opera Cancels All Fall Performances',
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{'name': 'subject',
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'pub_date': '2020-06-01T20:00:13+0000',
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{'abstract': 'While many in the country abhor the kind of harassment Hana Kimura endured before her apparent suicide, some warn that cracking down on it could also chill rising internet activism.',
'web_url': 'https://www.nytimes.com/2020/06/01/business/hana-kimura-terrace-house.html',
'snippet': 'While many in the country abhor the kind of harassment Hana Kimura endured before her apparent suicide, some warn that cracking down on it could also chill rising internet activism.',
'lead_paragraph': 'TOKYO — During the last weeks of Hana Kimura’s life, a steady stream of hate washed over her social media accounts. On Twitter, Instagram and Facebook, anonymous posters attacked her appearance, her outspoken behavior and especially her role on “Terrace House,” a popular Japanese reality show where some viewers saw her as a villain.',
'print_section': 'B',
'print_page': '3',
'source': 'The New York Times',
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{'abstract': 'These essays on American authors — some well known, some unjustly forgotten, some perpetually misunderstood — aim to restore a sense of the “complex fate” of being an American. Read more about the series.',
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{'abstract': 'A lawyer for the trial judge told an appeals court why he wanted to examine the Justice Department’s “unusual” request to dismiss the charge.',
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'snippet': 'A lawyer for the trial judge told an appeals court why he wanted to examine the Justice Department’s “unusual” request to dismiss the charge.',
'lead_paragraph': 'WASHINGTON — The Justice Department’s conduct in abruptly deciding to end the case against President Trump’s former national security adviser Michael T. Flynn was so unusual that it raised a “plausible question” about the legitimacy of the move, a lawyer for the trial judge overseeing that case told a federal appeals court on Monday.',
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'headline': {'main': 'Judge Asks Court Not to ‘Short Circuit’ His Review of Flynn Case',
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'value': 'Russian Interference in 2016 US Elections and Ties to Trump Associates',
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{'name': 'persons', 'value': 'Barr, William P', 'rank': 8, 'major': 'N'},
{'name': 'persons',
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'pub_date': '2020-06-01T22:14:51+0000',
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'byline': {'original': 'By Charlie Savage',
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{'abstract': 'Trade is raising money for its roasters’ communities with Come Together Coffee.',
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'snippet': 'Trade is raising money for its roasters’ communities with Come Together Coffee.',
'lead_paragraph': 'Trade, a coffee subscription service that delivers directly from roasters to consumers, is featuring a charitable effort this month to benefit the communities where some of its more than 50 roasters live. In cities across the country, 20 companies have come up with limited-edition roasts for a lineup called Come Together Coffee. Customers can select the roaster from a list that rotates weekly; the profits from the sale will be donated to a charity the roaster chooses. The coffees are $18 for 12 ounces, and a subscription is not required for purchase. Joe Coffee is a participating roaster in New York, with a medium-bodied Peruvian coffee that has pleasing fruity elements.',
'print_section': 'D',
'print_page': '3',
'source': 'The New York Times',
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'headline': {'main': 'A Coffee Club Fund-Raises With Special Blends',
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'pub_date': '2020-06-01T22:15:10+0000',
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'byline': {'original': 'By Florence Fabricant',
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{'abstract': 'Charlito’s Cocina is selling its trufa seca, a 1.5-pound black truffle salami, to the public.',
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'snippet': 'Charlito’s Cocina is selling its trufa seca, a 1.5-pound black truffle salami, to the public.',
'lead_paragraph': 'Ten years ago, Charles Wekselbaum was out of a job and decided to pursue his fascination with European meat-curing traditions. He stocked up on heritage pork, and a year later, he started Charlito’s Cocina to sell his handmade dry-cured salami. The story behind the company name? In addition to being Jewish-American, he’s also part Cuban. A factory in Gloversville, N.Y., produces an array of salami, dry-cured and long-lasting. The salamis are aged at his base of operations in Long Island City, Queens. He is now offering the large, 1.5-pound black truffle (trufa seca) salami ($45), usually reserved for restaurants, to the general public. It conveys just enough of that distinctively funky, truffly perfume and taste. Drink a natural red alongside. It’s available along with the rest of his varieties, in some stores and online.',
'print_section': 'D',
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'source': 'The New York Times',
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'headline': {'main': 'Add a Restaurant-Size Salami to Your Table',
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'print_headline': 'To Slice: Variety of Salamis for Your Table',
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'pub_date': '2020-06-01T22:16:37+0000',
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'byline': {'original': 'By Florence Fabricant',
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{'abstract': 'Brian P. Brooks, the acting head of the Office of the Comptroller of the Currency, warned state and local leaders that requiring face masks could help bank robbers.',
'web_url': 'https://www.nytimes.com/2020/06/01/business/coronavirus-banks-masks.html',
'snippet': 'Brian P. Brooks, the acting head of the Office of the Comptroller of the Currency, warned state and local leaders that requiring face masks could help bank robbers.',
'lead_paragraph': 'The new head of a powerful banking regulator is not letting his first full week on the job pass quietly, warning that measures meant to contain the spread of the coronavirus — including mandates for the use of masks in public — could endanger the financial system.',
'print_section': 'B',
'print_page': '4',
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'headline': {'main': 'New Regulator Warns Health Measures, Like Masks, May Hurt Banks',
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'major': 'N'},
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{'name': 'subject',
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{'name': 'persons', 'value': 'Brooks, Brian P', 'rank': 9, 'major': 'N'}],
'pub_date': '2020-06-01T22:20:30+0000',
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'byline': {'original': 'By Emily Flitter',
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{'abstract': 'A new offering from Planet Oat Oatmilk is silky smooth and comes in six flavors.',
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'snippet': 'A new offering from Planet Oat Oatmilk is silky smooth and comes in six flavors.',
'lead_paragraph': 'Planet Oat Oatmilk has introduced nondairy frozen desserts in six flavors: vanilla, chocolate, cookies and crème, coffee fudge swirl, chocolate peanut butter swirl and blueberry oat crumble. The collection of six is notable for its smooth texture; they behave like ice cream. Aside from the rather sweet blandness of the vanilla, the others are very appealing.',
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'headline': {'main': 'Oat Milk Powers This Ice Cream',
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'print_headline': 'To Scoop: Oat Milk Powers This Ice Cream',
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'pub_date': '2020-06-01T22:23:06+0000',
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{'abstract': 'Angelic Bakehouse has added tiny buns, made from seven sprouted whole grains, to its sandwich-bread lineup.',
'web_url': 'https://www.nytimes.com/2020/06/01/dining/angelic-bakehouse-slider-buns.html',
'snippet': 'Angelic Bakehouse has added tiny buns, made from seven sprouted whole grains, to its sandwich-bread lineup.',
'lead_paragraph': 'I’ve left Martin for another slider bun. Angelic Bakehouse in Cudahy, Wis., makes its new ones with seven sprouted whole grains and whole-wheat flour, but no chemical additives. All taste mildly wheaty, adding character to summer’s grilled meat patties and sausages. The buns are vegan and kosher. The company also makes wraps, pizza crusts and crackers and has a matching bread donation program for those in need.',
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{'abstract': 'Washington’s elite has long bemoaned the “shattered norms” of the Trump presidency. But it has felt beside the point — like privilege talking — in the angry crowds of the last few nights.',
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{'abstract': 'The pandemic-cooking craze hasn’t swept up everybody. To those who feel lost in the kitchen, even toasting bread and warming pizza are a challenge.',
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{'abstract': 'The more you read, the more you realize Shakespeare is woven into the fiber of the English language.',
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{'abstract': 'In 1957, Franklin Kameny was fired from a government job for being gay. His bold fight against bias, Eric Cervini shows in “The Deviant’s War,” inspired a movement.',
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{'abstract': 'A selection of books published this week; plus, a peek at what our colleagues around the newsroom are reading.',
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'lead_paragraph': 'MIDNIGHT LA FRONTERA, by Ken Light. (TBW Books, $55.) Alongside José Ángel Navejas’s harrowing introduction about his own migration, Light’s black-and-white photos reveal the dangers and despair he witnessed at the U.S. southern border from 1983 to 1987.',
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{'abstract': 'From “Do the Right Thing” to “Inside Man,” here is a starter guide to an imposing and eclectic body of work.',
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{'abstract': 'An excerpt from “The Next Great Migration,” by Sonia Shah',
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{'abstract': 'Chinese officials and state media are seizing on the moment to tout Beijing’s authoritarian system and condemn American hypocrisy — a narrative that ignores many of their own issues.',
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'headline': {'main': 'A Balancing Act for California’s Democratic Leaders',
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{'abstract': 'In a speech in Philadelphia, Mr. Biden assailed the president’s handling of the protests over police brutality and racial justice, declaring that he had “turned this country into a battlefield.”',
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'lead_paragraph': 'Joseph R. Biden Jr. on Tuesday excoriated President Trump’s stewardship of a nation convulsed in crisis over racism and police brutality, likening Mr. Trump’s language to that of Southern racists of the 1960s while also warning Americans that “we cannot let our rage consume us.”',
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{'abstract': 'The musician shares the movies, podcast, book and music that top her list of cultural essentials.',
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'lead_paragraph': 'With plans to release two albums in the first half of the year alone, Norah Jones expected to be busy this spring playing for live audiences. On Valentine’s Day, she put out “Sister” with Puss n Boots, her easy-rocking trio featuring Sasha Dobson and Catherine Popper. Its tour was, of course, canceled. And on June 12, Jones will release her eighth album as a leader, “Pick Me Up Off the Floor”; that too was supposed to be accompanied by concerts throughout the spring and summer, this time alongside Mavis Staples.',
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{'abstract': 'As the Metropolitan Opera announces that it will remain closed through the year, our critic reflects on the chorus scenes he will miss the most.',
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'lead_paragraph': 'Most of Verdi’s “Aida” is focused on intensely dramatic scenes for only one, two or three singers at a time. But it’s crowds that define the experience of this opera.',
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{'abstract': 'Find a song you collectively love that is fun to sing. You don’t have to become a music theorist, but learn to harmonize.',
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'lead_paragraph': '“Get your hands on an instrument,” says Danielle Haim, 31, who sings lead vocals and plays guitar in the Los Angeles-based band Haim, along with her sisters Este, 34, and Alana, 28. It helps to have musical parents who leave instruments around. Growing up in the Haim house, the sisters all started with the drums because their father kept a drum set in the living room.',
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{'abstract': 'Policymakers in the 1960s had the answers — give political and economic power to the people — but walked away.',
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'lead_paragraph': 'The circumstances that led to the police killings of George Floyd — and thousands of other citizens over the years — could have been avoided if our elected officials in the 1960s had responded to protesters’ demands for socioeconomic inclusion. Instead, policymakers blamed black people for the instability, ignoring the buildup of centuries of racial oppression. They pursued a misguided policy path that has failed to keep communities of color safe for more than 50 years.',
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{'abstract': 'Need something to occupy your mind and your time? Consider two book-themed diversions that grab your attention using post or pixel.',
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'lead_paragraph': 'Games that involve a lot of text may not be for everyone, but two particular amusements with similar names — “Dear Holmes” and “Dear Reader” — might appeal to book lovers searching for entertaining ways to pass the time. And while both of these endeavors can get brains buzzing, the former uses a popular 19th-century approach to content delivery, while the latter is firmly planted in the 21st century.',
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{'abstract': 'Isolation rooms, crib dividers and designated school shoes are just a few examples of the health measures that child care centers are taking.',
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'lead_paragraph': 'When I dropped my 3-year-old daughter off at school in mid-May for the first time in more than two months, we embarked on a new routine. She climbed out of the car with a mask over her mouth, refusing to pull it over her nose, and stood in line waiting to be checked in. When it was our turn, a teacher clad in gloves and a mask asked if my daughter had experienced a fever, cough, loss of smell or other coronavirus symptoms, and then took her temperature. After I gave her a hug, she was given a healthy pump of hand sanitizer and then escorted into the building. I waved goodbye from the sidewalk as the wooden gate shut behind her.',
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'lead_paragraph': 'It has been a week since thousands started marching in the streets to protest the killing of George Floyd in Minneapolis. And yet already a routine has emerged: Each night, tens of thousands exercise their right to assemble in protest and millions of Americans follow along at home.',
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'lead_paragraph': 'A “soft opening” for a new restaurant once meant easing into business with limited reservations for friends, family and staff. But, in today’s world, it means starting with takeout. Such is the case for Forsythia at 9 Stanton Street. “In the middle of construction everything went on pause,” said Jacob Siwak, formerly of Olmsted, who is the executive chef and owner. For the rustic Italian restaurant, where Mark Coleman, formerly of Rezdôra, is sous chef, and Brian Maxwell, of Gulfstream in Newport Beach, Calif., is baking, they are waiting until indoor dining restrictions are lifted before opening. In the meantime, it is offering takeout from a temporary East Village location, in a space that’s usually a cooking school. The menu is based on a working sojourn in Italy the men took before the coronavirus pandemic, and has Roman overtones. Focaccia with mortadella, short rib meatballs, supplì cheese and rice balls with asparagus and mushrooms, stuffed pastas like scarpinocc and tortellini, and a brioche bun filled with whipped cream are some of the dishes they’re planning and preparing for four-course takeout menus, $30 per person. The kitchen is open from 4:30 to 6 p.m. daily, except Sundays. Once the Stanton Street location opens, they will close the East Village location and use the bar in front of the Lower East Side restaurant as a takeout counter. ',
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'lead_paragraph': 'If Jessica Kramer didn’t already know that she had started to shop in a new way, the personal letter she got from the meat and poultry supplier D’Artagnan would have been a tip-off.',
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{'abstract': 'The retailer, whose Herald Square location looms larger perhaps than any other store in New York, was already reeling from closings caused by the pandemic.',
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'lead_paragraph': 'In the end, the damage to the store may have been limited. But images of looters smashing windows and running through Macy’s flagship location in Herald Square was another symbolic hit to the already badly battered retailer.',
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'lead_paragraph': 'As protests over the killing of George Floyd (and Ahmaud Arbery and Breonna Taylor) spill into a second week, many parents are wondering how to talk about the deaths and unrest with their children. But just as important in the long run, especially for nonblack parents, is how to keep the conversation about race and racism going when we’re not in a moment of national outrage, and to make sure all children see black people as heroes in a wide range of their own stories, and not just as victims of oppression.',
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'lead_paragraph': 'To the Editor:',
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{'abstract': 'Twitter and Facebook enable truth telling, but there are also downsides to having no gatekeepers.',
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'lead_paragraph': 'This article is part of the On Tech newsletter. You can sign up here to receive it weekdays.',
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{'abstract': 'The U.S. investigation targets nine countries, plus the European Union, that have adopted or are considering new taxes that would hit American companies like Google and Amazon.',
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'lead_paragraph': 'WASHINGTON — The Trump administration said on Tuesday that it would open an investigation into taxes on digital commerce that have been adopted or proposed in nine countries and the European Union, escalating a global battle that will affect where big American tech companies like Facebook and Amazon pay taxes.',
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'lead_paragraph': 'This article is part of the Debatable newsletter. You can sign up here to receive it Tuesdays and Thursdays.',
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'lead_paragraph': 'On Nov. 9, 2016, the morning after our last presidential election, my column began by recalling words from an immigrant, my friend Lesley Goldwasser, who came to America from Zimbabwe in the 1980s. Surveying our political scene a few years earlier, Lesley had remarked to me: “You Americans kick around your country like it’s a football. But it’s not a football. It’s a Fabergé egg. You can break it.” I then added: “With Donald Trump now elected president, I have more fear than I’ve ever had in my 63 years that we could do just that — break our country, that we could become so irreparably divided that our national government will not function.”',
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'lead_paragraph': 'Jamila Thomas and Brianna Agyemang, two black women who work in music marketing, proposed that the music industry hold a day of reflection on Tuesday after the death of George Floyd.',
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{'abstract': 'Corrections that appeared in print on Wednesday, June 3, 2020.',
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'lead_paragraph': 'Every afternoon, I get a ping on my computer announcing the number of days since my hard drive was backed up in Paris, where I live part time. That ping means it has been 128 days since I was in Paris — the longest I’ve been away from that city and my friends in decades. One hundred and twenty-nine days since I had a haircut; I think the last time I had to push my hair out of my eyes, the Beatles were still together. And a record 122 dinners and counting that I’ve cooked at home. (I’ve subtracted the three meals we had in restaurants in February and the three at friends’ homes later that month.)',
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'lead_paragraph': 'The play unfolded with precision, like so many of Borussia Dortmund’s goals have this season. A nudge with the side of a foot sent the ball from the middle of the field down the right wing. A perfectly placed cross put it, on a single hop, into the path of Erling Haaland, as he approached the penalty area on a sprint. And then Haaland, a 19-year-old Norwegian striker who seems to be on the cusp of becoming world soccer’s next great scorer, redirected it into the corner of the net with a left-footed flick.',
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{'abstract': 'Even as mayor, I know what every other parent to a black boy in America knows: My son is simply who he is, a young black man.',
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'lead_paragraph': 'ATLANTA — I frantically screamed into the phone to my teenage son: “Lance, WHERE ARE YOU?!”',
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{'abstract': 'When every day is Blursday, men’s colognes give meaning and shape to a week.',
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'lead_paragraph': 'Today, I am feeling Hinoki. It is Monday, or at least I think so. Nine weeks into lockdown, each morning dawns another Blursday. Time torques, the compass shrinks and with it the conviction that a horizon exists and, beyond it, an end to the pandemic.',
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{'abstract': 'They shouldn’t lose hope. They are at the heart of the fight to take back America.',
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'lead_paragraph': 'In Columbia, S.C., on Saturday, a young protester told a reporter that she just didn’t think voting is “how change happens.”',
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{'abstract': 'Brands are trying to show solidarity with customers’ strongly held values without giving offense.',
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'lead_paragraph': 'Want this in your inbox each morning? Sign up here.',
'source': 'The New York Times',
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{'abstract': 'Publishers are increasingly seeking out audio talent that reflects the race and experience of the books’ authors and characters. But what constitutes a black, Latino or Asian voice?',
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'snippet': 'Publishers are increasingly seeking out audio talent that reflects the race and experience of the books’ authors and characters. But what constitutes a black, Latino or Asian voice?',
'lead_paragraph': 'When the actor and audiobook narrator Cary Hite learned he had been cast to read a novella for a sci-fi anthology, he was ecstatic — and not just because he loved the genre.',
'print_section': 'C',
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'source': 'The New York Times',
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'headline': {'main': 'As Audiobook Market Grows, Narrators of Color Find Their Voice',
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{'name': 'subject', 'value': 'Voice and Speech', 'rank': 4, 'major': 'N'},
{'name': 'subject',
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{'name': 'subject', 'value': 'Minorities', 'rank': 6, 'major': 'N'},
{'name': 'subject',
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{'name': 'subject',
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{'name': 'organizations',
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'pub_date': '2020-06-03T12:00:12+0000',
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'byline': {'original': 'By Fabrice Robinet',
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{'abstract': 'These books can help start the conversation.',
'web_url': 'https://www.nytimes.com/2020/06/03/parenting/kids-books-racism.html',
'snippet': 'These books can help start the conversation.',
'lead_paragraph': 'As protests over the killing of George Floyd (and Ahmaud Arbery and Breonna Taylor) spill into a second week, many parents are wondering how to talk about the deaths and unrest with their children. But just as important in the long run, especially for nonblack parents, is how to keep the conversation about race and racism going when we’re not in a moment of national outrage, and to make sure all children see black people as heroes in a wide range of their own stories, and not just as victims of oppression.',
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'headline': {'main': 'Talking to Kids About Racism, Early and Often',
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{'name': 'subject',
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{'name': 'subject',
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{'abstract': 'This Netflix documentary explores the unbroken Indian-American dominance of the U.S. national spelling bee, to less than winning results.',
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'snippet': 'This Netflix documentary explores the unbroken Indian-American dominance of the U.S. national spelling bee, to less than winning results.',
'lead_paragraph': 'If you’re browsing for feel-good stories of American diversity nowadays, there is nothing more inoffensively comforting than Netflix’s new documentary “Spelling the Dream.” Celebrating the unbroken winning streak of Indian-Americans at the Scripps National Spelling Bee, Sam Rega’s film follows four Indian-American regional contestants in their quest for a spot on the stage of the 2017 national finale.',
'print_section': 'C',
'print_page': '6',
'source': 'The New York Times',
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'headline': {'main': '‘Spelling the Dream’ Review: Words of Inspiration',
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{'name': 'persons', 'value': 'Rega, Sam', 'rank': 3, 'major': 'N'},
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'pub_date': '2020-06-03T12:44:10+0000',
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{'abstract': 'We analyzed the president’s Twitter feed for a week. A third of his posts contained falsehoods or murky accusations, underscoring the challenge to Twitter’s chief, Jack Dorsey, of policing him.',
'web_url': 'https://www.nytimes.com/2020/06/03/us/politics/trump-twitter-fact-check.html',
'snippet': 'We analyzed the president’s Twitter feed for a week. A third of his posts contained falsehoods or murky accusations, underscoring the challenge to Twitter’s chief, Jack Dorsey, of policing him.',
'lead_paragraph': 'Twitter and its chief executive, Jack Dorsey, placed warnings on three of President Trump’s tweets last week, taking a measured but hotly debated step to place some limit on the president’s use of social media to spread falsehoods and incite his followers.',
'print_section': 'A',
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'source': 'The New York Times',
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'headline': {'main': 'Hey @jack, Here Are More Questionable Tweets From @realdonaldtrump',
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'pub_date': '2020-06-03T12:47:51+0000',
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{'abstract': 'Therapy and fancy vacations couldn’t remove the strain on this couple’s marriage, which was brought about by ongoing financial problems.',
'web_url': 'https://www.nytimes.com/2020/06/03/fashion/weddings/Unhitched-couple-discusses-their-marriage-and-divorce.html',
'snippet': 'Therapy and fancy vacations couldn’t remove the strain on this couple’s marriage, which was brought about by ongoing financial problems.',
'lead_paragraph': 'In Unhitched, couples tell the stories of their relationships, from romance to vows to divorce to life afterward.',
'print_section': 'ST',
'print_page': '11',
'source': 'The New York Times',
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'headline': {'main': 'Challenged to Sustain Their Lifestyle',
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'pub_date': '2020-06-03T12:53:06+0000',
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{'abstract': 'Galleries and museums are getting creative about presenting work online during the coronavirus crisis. Here are two shows worth viewing virtually.',
'web_url': 'https://www.nytimes.com/2020/06/03/arts/design/virtual-art-shows.html',
'snippet': 'Galleries and museums are getting creative about presenting work online during the coronavirus crisis. Here are two shows worth viewing virtually.',
'lead_paragraph': 'If an online exhibition can’t replicate the joy and surprise of seeing art in person, it has one big advantage over a gallery show: You don’t need to move all the stuff! The artists Julie Ault and Martin Beck have spent the lockdown isolated in Joshua Tree, Calif., where they live with all sorts of art, artifacts and detritus made by James Benning, a filmmaker and longtime professor at CalArts. When the pandemic began, these two artists photographed, cataloged and captioned everything Mr. Benning, their friend and frequent houseguest, had given them, whether it was on their walls or in their kitchen cabinets. And the resultant presentation, hosted on the website of the Los Angeles project space O-Town House, is a rare example of an online show that feels native to the web: a slow and intimate house tour that also maps a community of artists.',
'print_section': 'C',
'print_page': '6',
'source': 'The New York Times',
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'headline': {'main': '2 Art Gallery Shows to Explore From Home',
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{'name': 'subject', 'value': 'Photography', 'rank': 2, 'major': 'N'},
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'value': 'Ault, Julie (1957- )',
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'value': 'JOSHUA TREE (CALIF)',
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{'name': 'subject',
'value': 'Migrants (Environmental)',
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'value': 'Benning, James (1942- )',
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{'name': 'persons',
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{'name': 'persons', 'value': 'Jacome, Felipe', 'rank': 11, 'major': 'N'}],
'pub_date': '2020-06-03T13:00:13+0000',
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'_id': 'nyt://article/dfd76633-1ac0-55bf-94dc-ed672eadf25b',
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{'abstract': 'A 1961 glass house in Chardon, Ohio; a two-bedroom condo in Pittsburgh; and a 1924 Prairie-style duplex in Indianapolis.',
'web_url': 'https://www.nytimes.com/slideshow/2020/06/03/realestate/what-you-get-for-600000.html',
'snippet': 'A 1961 glass house in Chardon, Ohio; a two-bedroom condo in Pittsburgh; and a 1924 Prairie-style duplex in Indianapolis.',
'lead_paragraph': 'A 1961 glass house in Chardon, Ohio; a two-bedroom condo in Pittsburgh; and a 1924 Prairie-style duplex in Indianapolis.',
'source': 'The New York Times',
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'headline': {'main': 'What You Get for $600,000',
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'pub_date': '2020-06-03T13:00:32+0000',
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{'abstract': 'A 1961 glass house in Chardon, a two-bedroom condo in Pittsburgh and a 1924 Prairie-style duplex in Indianapolis.',
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'lead_paragraph': 'John Terence Kelly, an architect who was a roommate of Andy Warhol at Carnegie Institute of Technology and a graduate student of Walter Gropius at Harvard, designed this two-story house for a veterinarian and his family. It is built into a hillside, with south-facing glass walls and an overhanging roof, according to the energy-saving principles of passive construction.',
'print_section': 'RE',
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'source': 'The New York Times',
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'headline': {'main': '$600,000 Homes in Ohio, Pennsylvania and Indiana',
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{'abstract': 'Graduation can be exciting or sad or somewhere in between. Here’s how to mark your accomplishments.',
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'snippet': 'Graduation can be exciting or sad or somewhere in between. Here’s how to mark your accomplishments.',
'lead_paragraph': 'It’s graduation season — and, understandably, parents, friends, classmates and the graduates themselves are at a loss for how to celebrate. For the 3.7 million expected to graduate high school this year, streaming a commencement address by a prominent politician or celebrity isn’t a substitute for the feeling you get walking across a stage and receiving a hard-earned diploma. Smiling for photos, surrounded by classmates, enjoying a meal at a restaurant with your family after the ceremony … those are just some of the graduation rituals that have been put on hold for the time being.',
'source': 'The New York Times',
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{'abstract': 'The latest on stock market and business news during the coronavirus outbreak.',
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'lead_paragraph': 'This briefing is no longer updating. The latest developments can be found here.',
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{'abstract': 'A play based on Charles W. Chesnutt’s “Marrow of Tradition” shows the writer of “A Raisin in the Sun” attuned to the history of white violence.',
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{'abstract': 'The U.S. looks weak. Business is falling in line. Protests have been muted. For Beijing, the damage to the city and its own reputation from seizing greater control may be worth it.',
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'lead_paragraph': 'HONG KONG — China long depended on Hong Kong to be everything it was not. The city’s freewheeling capitalism and personal freedoms, both absent from the mainland, made it one of the world’s premier financial hubs. Together, they flourished for decades.',
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{'abstract': 'Demonstrators had tried to topple the statue of Frank Rizzo, the former mayor and police commissioner accused of police abuses in the 1960s and ’70s.',
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'headline': {'main': 'Philadelphia Removes Statue Seen as Symbol of Racism and Police Abuse',
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'lead_paragraph': 'Good morning. Concentration is hard to come by these days, amid the nation’s strife. We are living through a tough and chaotic and wrenching time, filled with fury and an abiding sadness. We’re unsettled. We’re tense. We’re divided. The emotions arrange themselves in combinations that make it hard to work, to read, to watch, to listen, much less to think.',
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'headline': {'main': 'Musicians Push Industry to Support Justice With Money, Not Hashtags',
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'lead_paragraph': 'WASHINGTON — President Trump and his aides spent much of Wednesday trying to rewrite history, claiming that Mr. Trump was merely “inspecting” a bunker last week during riots over the death of George Floyd and insisting falsely that peaceful protesters near the White House were attacking the police when the authorities used chemical agents to make them move so that Mr. Trump could have his picture taken at a nearby church.',
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{'abstract': 'The list could have been twice as long. But this should help you prioritize.',
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'lead_paragraph': 'The timing is presumably accidental, owing to the vagaries of licensing agreements rather than nefarious motives, but it still seems downright cruel that so many television series and films of note are departing Netflix as so many of us are still stuck at home. Here are our suggestions for what to prioritize in that ever-shrinking queue. (Dates indicate the final day a title is available.)',
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{'abstract': 'Though the event was scuttled by a global pandemic, organizers have released an official lineup with movies from Wes Anderson and others.',
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'lead_paragraph': 'The 73rd edition of the Cannes Film Festival was supposed to be a big one: A jury led by Spike Lee would have spent two weeks in May picking a Palme d’Or winner to follow “Parasite,” the Bong Joon Ho thriller that debuted on the Croisette last year before it went on to win the best-picture Oscar.',
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{'abstract': 'The plan, expected to be easily adopted on Thursday, would give six outside teams a chance to win their way into the playoffs.',
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'headline': {'main': 'The Videos That Rocked America. The Song That Knows Our Rage.',
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'lead_paragraph': 'The handshake came first. Then the high-five, fist bump and more recently, the elbow touch. Canadian researchers are now working on a new greeting, the CanShake.',
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'pub_date': '2020-06-03T18:10:59+0000',
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{'abstract': 'M.L.B. and the players’ union are at risk of turning a potential opportunity into “a gigantic negative,” Bob Costas said. Time is running low to avoid that fate.',
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{'abstract': 'The Post and Courier in Charleston requires temperature checks at the door. An editor said she had been fired after approving a reader’s comment criticizing the newspaper’s shift away from remote work.',
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'headline': {'main': 'Wary Journalists Return to a Reopened South Carolina Newsroom',
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'lead_paragraph': 'A remarkable range of theater has worked its way into our homes over the past three months, and it has certainly been soothing to enjoy some razzmatazz — nothing distracts from reality like a powerhouse belt.',
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{'abstract': 'In this week’s newsletter, Stein discusses the N.B.A.’s plans to try to return from the coronavirus pandemic, with the backdrop the continuing reaction to the killing of George Floyd in Minneapolis.',
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'lead_paragraph': 'Want more basketball in your inbox? Sign up for Marc Stein’s weekly N.B.A. newsletter here.',
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{'abstract': 'This simple weeknight dish gets its bold flavor and texture from garlic- and anchovy-infused bread crumbs.',
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'lead_paragraph': 'Listen and subscribe to our podcast from your mobile device: Via Apple Podcasts | Via RadioPublic | Via Stitcher',
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'lead_paragraph': 'THURSDAY PUZZLE — There are multiple ways to ramp up a puzzle’s difficulty. There are those complex, high-wire act themes on Thursdays that so many of us love. Vocabulary and general knowledge levels can be toughened up. Clues can be written in such a way that the answer is not immediately recognizable. The editors could suddenly start publishing the puzzles in Swedish. My point is, they have a range of choices they can make.',
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{'abstract': 'Quotation of the Day for Thursday, June 4, 2020.',
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{'abstract': 'Corrections that appeared in print on Thursday, June 4, 2020.',
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'lead_paragraph': 'An article on Wednesday about protests against police brutality evolving into a nationwide movement with diverse interests and tactics misidentified the location of an episode at a protest in Baltimore. It was outside City Hall, not outside the governor’s mansion.',
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{'abstract': 'A survivor of polio, she created a place for disabled women to earn a little money, and find solidarity. Doctors believe she died because of Covid-19.',
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'headline': {'main': 'Celine Fariala Mangaza, Congolese Heroine of Disabled People, Dies at 52',
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{'abstract': 'Residents across the city gathered to commemorate the victims of China’s 1989 crackdown, despite a police ban. Hours earlier, the city made mocking China’s anthem a crime.',
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'lead_paragraph': 'Chanting slogans like “Liberate Hong Kong,” thousands of people in Hong Kong flouted a police ban on Thursday as they gathered to memorialize the Tiananmen Square massacre, a striking display of defiance against Beijing’s tightening grip on the territory.',
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{'abstract': 'Here’s what you need to know.',
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'lead_paragraph': 'SPELLING THE DREAM (2020) Stream on Netflix. If someone walked up to you on the street and asked you to spell the name of the state fish of Hawaii by its Hawaiian name, humuhumunukunukuāpuaʻa, could you do it? How about when you were 6 years old? Such intricate words are sprinkled throughout this upbeat documentary, which looks at a yearslong winning streak of Indian-Americans at the Scripps National Spelling Bee. The film splits its attention among four young contestants, interspersing their stories with commentary from figures including the comedian Hari Kondabolu and Sanjay Gupta, CNN’s chief medical correspondent. “Inoffensively comforting” is the way Bilal Qureshi described the movie in his review for The New York Times. “Spelling the Dream,” he wrote, “is a film about winning, delivered with glossy visuals and a gratingly optimistic score that draws to a close with its champion showered in confetti — an obvious symbol for this overarching (and under-questioned) celebration of American multiculturalism.”',
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{'abstract': 'The North compared the United States to a setting sun, taking notice of the unrest over George Floyd’s killing. It also threatened to scrap deals with South Korea.',
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{'abstract': 'The victims included 37 students, officials said. China has seen a rash of similar attacks on schoolchildren in recent years.',
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{'abstract': 'A settler himself, Avigdor Liberman taunts Israel’s prime minister for talking endlessly about annexing West Bank territory, giving opponents time to mobilize.',
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'lead_paragraph': '“It’s done so much damage in fashioning savage notions of Africa,” says the author of “Jay-Z: Made in America” and many other books.',
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'headline': {'main': 'Why Michael Eric Dyson Would Demote ‘Heart of Darkness’ From the Canon',
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'lead_paragraph': 'Want this in your inbox each morning? Sign up here.',
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{'abstract': 'Officers were forceful in dispersing demonstrators who stayed out past curfew in the Bronx, Manhattan and Brooklyn.',
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'lead_paragraph': '[This briefing has ended. For the latest updates on the George Floyd protests in New York City, read Friday’s live coverage.]',
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{'abstract': 'Just hear me out.',
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{'abstract': 'In this bizarre satire, married puppeteers enact a grotesque comic tragedy of violence and revenge.',
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'lead_paragraph': 'I ran into a friend in the lobby of our building. (I thought we were friends, anyway.) She is black; I am white. And protests over racist policing have been raging in our city. First, I made sure her teenage son was safe. I was worried after seeing news reports. (He’s fine.) Then I told her I was sad to see the protests turn violent, that I sincerely believe violence and hatred only beget more of the same. At that point, she rolled her eyes, said she envied me and walked away. Did I do something wrong?',
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{'abstract': '“If you ask me today if I want to travel today to New York to play a tennis tournament, I will say no, I will not.”',
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'lead_paragraph': 'Traditionally, this is Rafael Nadal’s time to dominate: the week when the world’s premier clay-court tennis tournament, the French Open, comes to a close, and when Nadal secures another singles title.',
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{'abstract': 'Six years before the coronavirus, Neïl Beloufa imagined a global outbreak of disease and digital bewilderment. “Screen Talk,” his new project, erases the line between fiction and real life.',
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'lead_paragraph': 'The accomplished young French-Algerian artist Neïl Beloufa has a knack for seeing the shape of the future earlier than most. His proudly disjointed videos and sculptures, seen in this country in solo shows at the Museum of Modern Art in New York and the Hammer Museum in Los Angeles, take the chaos of our information stream as both their subject and their medium: furniture can sprout phone chats or Google Maps indicators, and lovers and criminals talk like they learned English from reality TV. He turns a gimlet eye, too, to the workings of fake news and real bigotry, as in his powerful project “Occidental” (2017), a film about racist policing and urban unrest that feels dismayingly relevant again this week.',
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{'abstract': 'Sixty-three years ago, American troops were deployed to quell racial unrest. It made this country a better place.',
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'lead_paragraph': 'Protests in Minnesota after the killing of George Floyd made the state “a laughingstock all over the world,” President Trump claimed in a call to state governors on Monday. He later pledged to bypass governors who tolerated protest, and to use the military to “dominate” American streets.',
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'headline': {'main': 'The Damage Trump Has Done This Week Extends Far Beyond America’s Borders',
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{'abstract': 'Our culture writers offer suggestions for what to watch or listen to while we’re housebound.',
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'lead_paragraph': 'The death of a black man, uprisings in the streets: Dance Theater of Harlem was born in a moment like this. It was the assassination of the Rev. Dr. Martin Luther King Jr. in 1968, and the ensuing anger and despair, that incited the African-American ballet star Arthur Mitchell to create the company. It was a mission of hope. To those who had been told they couldn’t dance ballet, he said, “You can.”',
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'lead_paragraph': 'This article is part of the On Tech newsletter. You can sign up here to receive it weekdays.',
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'lead_paragraph': 'When Mike moved from Vancouver to Silicon Valley in late 2018, he couldn’t believe his luck. He found a beautiful Art Deco cottage less than 30 minutes from work, with redwood forests and wineries on its doorstep and rent that was inexplicably within his price range. It was the dream home he and his wife, Jackie, had been searching for — perfect for the family they were about to start.',
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{'abstract': 'Our writer recommends “The House of Flowers,” “The Queen of Flow” and four other Spanish- and Portuguese-language shows now on Netflix and Hulu.',
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{'abstract': 'With unemployment soaring in this region of haves and have-nots, a local Boys and Girls Club has transformed into a pop-up takeout operation to feed the most disadvantaged.',
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'snippet': 'With unemployment soaring in this region of haves and have-nots, a local Boys and Girls Club has transformed into a pop-up takeout operation to feed the most disadvantaged.',
'lead_paragraph': 'PALO ALTO, Calif. — Andres Pantoja, an up-and-coming Silicon Valley sous chef, spent his pre-pandemic evenings delicately preparing the $115 plate of lamb chops and deboning the $42 Psari Plaki whole fish at a fashionable restaurant here. It is frantic work serving 200 upscale meals a night.',
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'headline': {'main': '2,000 Free Meals a Night, Seasoned by Silicon Valley Chefs',
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{'abstract': 'His anti-Semitic, anti-Vatican views gained notoriety after his son directed the controversial film, “The Passion of the Christ.”',
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'lead_paragraph': 'Hutton Gibson, a Roman Catholic traditionalist and outspoken critic of the modern church who gained wide notoriety as the father of the actor Mel Gibson and for his anti-Semitic views, died on May 11 in Thousand Oaks, Calif. He was 101.',
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{'abstract': 'While the president cloaks himself in spiritual authority, he preaches the antithesis.',
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'lead_paragraph': 'In a crucible moment of life or history, it’s important to keep focused on what matters most, lest the moment pass and we miss a transformational opportunity.',
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{'abstract': 'Video has become the central lens through which Americans have viewed the nationwide unrest that has followed the death of George Floyd. Here’s what we’re seeing.',
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'lead_paragraph': 'By their nature, books are ill equipped to bring you breaking news; that’s journalism’s job. But today’s unrest has deep roots, so you’ll find echoes and reverberations in a couple of our recommended titles this week. Brit Bennett’s new novel, “The Vanishing Half,” offers a thoughtful look at the complexities of racial identity and belonging in America, and Deirde Mask’s “The Address Book” details how something as seemingly innocuous as a street name can in fact speak volumes about society’s approach to race, class and historical memory.',
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{'abstract': 'Mr. McCormack, who died of coronavirus-related causes, lifted the careers of Off Broadway actors and playwrights, even as he remained elusive.',
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'lead_paragraph': 'This obituary is part of a series about people who have died in the coronavirus pandemic. Read about others here.',
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'headline': {'main': 'John McCormack, a Nurturing Theater Producer, Dies at 61',
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{'abstract': 'I started filming the protests in New York so I could draw attention to the realities of black America that I was first exposed to during my military service.',
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'lead_paragraph': 'I’m in film school, using the last bit of my G.I. Bill, and have been shooting a short film about five black artists surviving Covid-19. We wrapped shooting on the morning of Memorial Day, and that afternoon George Floyd was killed. A few days later, one of the artists I’m profiling headed to the streets in Brooklyn to protest, and it just felt like the responsible thing to show up with my camera and capture the stories.',
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'headline': {'main': 'Serving in the Army as a Queer Black Man Opened My Eyes to Racism in America',
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{'abstract': 'The senator suggested the former defense secretary’s scathing remarks might be a tipping point, prodding other Republicans to go public with longstanding concerns about President Trump.',
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'lead_paragraph': 'WASHINGTON — Senator Lisa Murkowski, Republican of Alaska, said on Thursday that she endorsed scathing criticism of President Trump’s leadership by Jim Mattis, the former secretary of defense, and was grappling with whether to support the president in the coming election.',
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'headline': {'main': 'Murkowski Endorses Mattis’s Criticism of Trump, Calling it ‘Necessary and Overdue’',
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'headline': {'main': 'Why Aren’t We All Talking About Breonna Taylor?',
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{'abstract': 'Miranda’s rap. Rylance’s poems. Jackman’s pelvis. And a brassy reunion for Bea Arthur and Angela Lansbury. Now set your clock for “Turkey Lurkey Time.”',
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'lead_paragraph': 'The 500 guests at the American Theater Wing dinner dance at the Plaza Hotel on April 1, 1956, were not the only ones who saw Gwen Verdon, Paul Muni, Bob Fosse and Lotte Lenya accepting their Tony Awards. So did fans all over New York City, who for the first time in the history of the honors, which began in 1947, could watch the event live, on DuMont channel 5.',
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{'abstract': 'The proposal approved by team owners will next go to the players’ union. The 22 teams would initially play eight games each to help determine the playoff field of 16.',
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'lead_paragraph': 'Listen and subscribe to our podcast from your mobile device:Via Apple Podcasts | Via Google Podcasts | Via Spotify',
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'headline': {'main': 'Hecky Powell, Barbecue Master and Civic Leader, Dies at 71',
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'lead_paragraph': 'Every Monday, Wednesday and Friday, our TV critic Margaret Lyons offers hyper-specific viewing recommendations in our Watching newsletter. Read her latest picks below, and sign up for the Watching newsletter here.',
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'lead_paragraph': 'What fills us up in disquieting times? What galvanizes through trauma? As the world endured months of lockdown because of the coronavirus pandemic, artists put forth works that comfort, that remind us of our regular days and nights at the theater and the gallery, of the bonhomie of a concert hall, the warm glow of a weekend movie.',
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'lead_paragraph': 'OAKLAND, Calif. — For the last few months, some people who bought a new smartphone in Europe with Google’s Android software were presented with an extra option while setting up the device: choosing a search engine other than Google.',
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{'abstract': 'The N.W.S.L.’s nine teams will gather for a new championship tournament later this month, but women’s soccer’s biggest star appears set to sit it out.',
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{'abstract': 'Republicans on two Senate committees moved to give their chairmen power to subpoena dozens of Obama administration and F.B.I. officials for the election-year inquiry.',
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{'abstract': 'Janet Mock, Trevor Noah and others featured in T share their favorite books.',
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'lead_paragraph': 'As the nation sees its 10th day of protest against the murder of George Floyd, various lists of books that address race, political upheaval and activism have been circulating on social media and elsewhere. They remind us that the work of combating systemic racism is never over, and that learning about the history of protest — and unlearning our own biases and prejudices — requires a lifetime of reading. In an effort to listen to black voices and encourage self-education, we’re revisiting these compilations, first published in 2016, of book recommendations from artists, leaders and writers of our time.',
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'headline': {'main': 'Reading Recommendations From Black Cultural Figures',
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{'abstract': 'Quotation of the Day for Friday, June 5, 2020.',
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'lead_paragraph': 'YOURSELF AND YOURS (2020) Watch on virtual cinemas. Alcohol and love infuse “Yourself and Yours,” a new movie from the South Korean filmmaker Hong Sang-soo. It kicks off with a breakup: Young-soo (Kim Ju-hyuk) tells Min-jung (Lee Yoo-young), his girlfriend, to quit drinking. She quits him instead. The plot that follows involves mystery and despondency. “Hong’s formal confidence yields a movie that’s very simply constructed and utterly engrossing,” Glenn Kenny wrote in his review for The New York Times. He named the film a Critic’s Pick. “There are a lot of scenes done in a single shot, usually static, but when there’s a zoom (his preferred camera flourish), it’s unfussy and direct,” Kenny wrote. “He puts you in tune with the world of his sad-sack characters immediately, and their rhythm becomes the rhythm of the story.” The film was released overseas in 2016, but is just now having its stateside debut; it’s available this weekend from many virtual cinemas, including Film at Lincoln Center’s.',
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'lead_paragraph': 'The Australia Letter is a weekly newsletter from our Australia bureau. Sign up to get it by email. This week’s issue is written by Damien Cave and Isabella Kwai, reporters in the Australia bureau who have both covered issues of race and identity.',
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{'abstract': 'Meyers fact-checked Trump’s claim to have christened the former defense secretary “Mad Dog”: “We know you didn’t come up with it because it’s a cool nickname.”',
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{'abstract': 'Six new paperbacks to check out this week.',
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'lead_paragraph': 'THE BODY IN QUESTION, by Jill Ciment. (Vintage, 192 pp., $15.) According to our reviewer, Curtis Sittenfeld, the “many pleasures” of Ciment’s novel about an affair between two jurors sequestered during a murder trial include “how knowingly but matter-of-factly Ciment depicts class distinctions,” her view of human fallibility and her unexpected ending. “I was left unsettled by this deft and gripping novel, and also deeply impressed.”',
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{'abstract': 'The abrupt switch to remote learning wiped out academic gains for many students in America, and widened racial and economic gaps. Catching up in the fall won’t be easy.',
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{'abstract': 'My mother scrubs toilets for a living. That makes her someone who has been deemed essential, but who has always been thought of as less than.',
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'lead_paragraph': 'I used to be afraid of cleaning toilets. As a teenager, it was my job to clean the upstairs bathroom while my two younger brothers were outside helping our father mow the lawn, pull weed and trim the bushes. My parents were Portuguese immigrants whose core belief system was rooted in a culture with patriarchal roles and traditional Christian values. I couldn’t take out the trash any more than my brothers could do the dishes.',
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{'abstract': 'Democrats can learn from the people who know Trump’s base best — the Never Trump Republicans.',
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'lead_paragraph': 'We are at the precipice, the us versus him moment. The people, protected by members of the military more faithful to the Constitution they have sworn to uphold than is their commander in chief. And him, the president, threatening to use that military against his own people.',
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{'abstract': 'A black hole was seen shooting electrified gas and energy into space. Each blob contained about 400 million billion pounds of matter.',
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{'abstract': 'The coronavirus outbreak has had a devastating impact on the livelihoods of Native American artists. But they are responding with a creative resolve born from centuries of adversity.',
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{'abstract': 'The live-in caretakers of some of New York’s cherished historic houses used to meet the city every morning from their front doors. Now they wait for that city to return.',
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{'abstract': 'The resident caretakers of some of New York’s cherished landmarks may have the city’s strangest work-from-home assignment.',
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'headline': {'main': 'Quarantined in a Museum',
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{'abstract': 'When it comes to death, Mexicans have deeply ingrained traditions.',
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{'abstract': 'The mayor of Tacoma, Wash., called for the firing and prosecution of officers involved in the arrest of Mr. Ellis after video clips of the encounter emerged.',
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{'abstract': 'American Ballet Theater and New York City Ballet offer virtual seasons. The reigning screen gem: Balanchine’s “Diamonds.” It’s like you were there.',
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'headline': {'main': 'Can Ballet Come Alive Online?',
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{'abstract': 'L.G.B.T.Q. characters star in these comic books and graphic novels that are available now, later this month and in the fall.',
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'lead_paragraph': 'Towns and cities across the United States have been convulsed in protest against police killings of black people. The president has declared that he is prepared to deploy the United States military to “dominate” the streets — while his secretary of defense says he opposes using military force against American civilians.',
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'lead_paragraph': '“People call me the Shrike.” Better call for police backup first, because the Shrike is the latest in a long line of supervillains created and lovingly nurtured by Michael Connelly. The Shrike makes his flashy debut in FAIR WARNING (Little, Brown, 404 pp., $29), where we find him murdering women by applying a brutal method of strangulation called atlanto-occipital dislocation. More women will die before the end of this narrative, all of them, as it turns out, victims of cyberstalking.',
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{'abstract': 'Video: How George Floyd died. At the C.D.C., what went wrong? Tara Reade’s journey. Pete Davidson is out of the basement. Farewell, Christo. And more.',
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'lead_paragraph': 'Welcome to the weekend. It’s been a week, right? I mean only a week? A week of not much light or levity, just some very tough stuff. I am hopeful that soon we will have some better days. Take care of yourself, and be sure to carve out some time for some great journalism.',
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{'abstract': 'Plus: How to be an ally.',
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'headline': {'main': 'Selling Toilet Paper and Paper Towels During the Pandemic',
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'lead_paragraph': 'Hello and welcome to Five Weeknight Dishes. Instead of my usual introduction, I want to direct you to a letter that Sam Sifton sent out today about NYT Cooking and our values. I hope you’ll take a moment to read it.',
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{'abstract': '“A president who takes no responsibility for costing millions and millions of Americans their jobs deserves no credit when a fraction of them return,” Mr. Biden said, adding that President Trump should “step out of his own bunker.”',
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{'abstract': 'Chewy, salty and run through with brown butter, these treats are ideal dessert material.',
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'lead_paragraph': 'I’ve been waking up very early lately. And sometimes, in those anxious predawn hours, I get an overwhelming desire to creep quietly into the kitchen and bake. This week, I craved chewy, salty butterscotch brownies, the kind made with browned butter, and almost enough dark brown sugar to make your teeth ache. But not quite.',
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{'abstract': 'The ruling came with assurance that the White House could remove any “rogue, mooning journalists” if such a disruption were to happen.',
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'lead_paragraph': 'WASHINGTON — A federal appeals court on Friday blocked the Trump administration from imposing a 30-day suspension on the White House press pass of a reporter for trading caustic and blustering comments with Sebastian Gorka, a right-wing commentator who worked briefly in the White House, after an event in the Rose Garden last year.',
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{'abstract': 'Worry less about the water, and more about the person standing next to you in a crowded locker room or wading in the shallow end as you swim by.',
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{'abstract': "For this issue, a look at the attorney general's long-held expansive view of presidential power and how he is putting his theories to work.",
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{'abstract': 'Here’s what you need to know at the end of the day.',
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{'abstract': 'The president’s comments on Mr. Floyd, who died after a police officer knelt on his neck for nearly nine minutes, were immediately criticized by Joseph R. Biden Jr., his Democratic opponent.',
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'headline': {'main': 'Harry Hoffman Dies at 92; Led the Expansion of Waldenbooks',
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{'abstract': 'The university was handed a ban of one year from the N.C.A.A. men’s basketball tournament, as well as fines and a loss of scholarships.',
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{'abstract': 'Demonstrators in New York remembered Breonna Taylor on what would have been her 27th birthday.',
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{'abstract': 'The police said that Anthony Brennan III, 60, of Kensington, Md., was arrested after investigators received hundreds of tips from people who had seen a video of the encounter.',
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{'abstract': 'Corrections that appeared in print on Saturday, June 6, 2020.',
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'lead_paragraph': 'An article on Thursday about a drop in the number of visits to emergency rooms during the coronavirus pandemic misstated in one instance the professional affiliation of Dr. William Jaquis. He is affiliated with the American College of Emergency Physicians, not Yale. The article also misattributed a quotation to him. It was Dr. Thomas Balcezak, the chief clinical officer for Yale New Haven Health, not Dr. Jaquis, who said: “It’s made the access to care even harder. We’re not seeing them in the emergency department.”',
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{'abstract': 'Quotation of the Day for Saturday, June 6, 2020.',
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{'abstract': 'A thriller starring Elisabeth Moss streams on Hulu, while the “Insecure” actress Yvonne Orji gets her first HBO special.',
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'lead_paragraph': 'SHIRLEY (2020) Stream on Hulu, or rent or buy on Amazon, Apple TV, FandangoNOW, Google Play or Vudu. This thriller transports us into the mind of the eccentric horror writer Shirley Jackson. Fred (Logan Lerman) and Rose (Odessa Young), a young couple, are offered to stay with Shirley (Elisabeth Moss) and her husband Stanley (Michael Stuhlbarg), a professor and literary critic, at their house in Vermont. But the arrangement slowly starts to feel like a trap. Stanley treats Fred as a professional grunt, while Shirley sees Rose as a muse for her next novel. The movie isn’t factual — the filmmaker Josephine Decker (“Madeline’s Madeline”) drew from the novel of the same name by Susan Scarf Merrell. But the formula works. In his review for The New York Times, A.O. Scott wrote that “Shirley is a mystery and a monster, and ‘Shirley’ is at once a sincere tribute and a sly hatchet job.”',
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{'abstract': 'A rip in the fabric of the economy won’t be healed easily, and denial of the severity of the crisis won’t solve it.',
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'lead_paragraph': 'Despite it all — a nation on edge, with an untamed pandemic and convulsive protests over police brutality — for the first time in three months there is a scent of economic optimism in the air.',
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'lead_paragraph': 'Even as states open up and, in some places, restaurants start to serve sit-down meals again, having friends in the house even for a short while remains a bad idea, experts say. Getting together outside — on blankets spaced apart in a park or on the deck or front porch or lawn at your home — is the way to go.',
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{'abstract': 'All you need is some running water and a splash of creativity to keep your family entertained.',
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'lead_paragraph': 'The end of the school year is approaching, and the usual summer diversions like a trip to the local pool are most likely out of the question. (How can you reliably stay six feet apart in the water?)',
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{'abstract': 'Of course, neighborhood associations are nothing new. But the current moment is a unique time to begin one.',
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'lead_paragraph': 'So you’re thinking about creating a neighborhood association. Shared crises have frequently galvanized community action, in formal and informal ways, and perhaps you’d like to corral that energy into organizing within your own area. These days, in the midst of the coronavirus pandemic, mutual aid networks have been incubated on Slack and ad hoc associations cultivated on the neighborhood social network Nextdoor — which also has the benefit of helping abate isolation.',
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'lead_paragraph': 'This live briefing has ended. Click here for the latest updates.',
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{'abstract': 'In Rob Harrell’s “Wink,” middle school social drama and an eyesight-threatening cancer are a lot for one kid to handle. Humor helps.',
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'lead_paragraph': 'Everybody feels like a freak in middle school. As the father of two teenagers who have recently emerged from puberty, I can tell you that children are freaks in middle school. Bodies stretch and bend like Silly Putty. An arm may grow six inches overnight. Emotions are even more malleable. No wonder so many of us look back on those years with about as much fondness as we have for cafeteria food. It makes sense, then, that teenagers want so badly to fit in. After all, when you feel like a freak, the desire to blend in with the crowd must be overwhelming. As Ross Maloy, the protagonist of Rob Harrell’s terrific new book, WINK (Dial, 320 pp., $16.99; ages 9 to 12), says: “Can I tell you how sick I am of being different? I hate it! You have no idea what I’d give to be normal.”',
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'headline': {'main': '‘Seventh Grade! Now in WonkyVision!’',
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{'abstract': 'In Lauren Wolk’s “Echo Mountain,” 12-year-old Ellie experiences both the horrors and the healing powers of nature when hard times force her family to return to the land.',
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'lead_paragraph': 'Lauren Wolk’s poetic new novel, ECHO MOUNTAIN (Dutton, 368 pp., $17.99; ages 10 and up), starts with a litter of puppies. One of them appears to be dead, and 12-year-old Ellie is supposed to bury him — but she will do no such thing. In a fit of bravery, she tries to revive the pup by plunging him into a bucket of water. He lives, and we know what we need to know: Ellie is a girl who takes matters into her own hands (and the book’s treatment of dogs is delightful).',
'print_section': 'BR',
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'headline': {'main': 'Girl Moves Mountains After Mountain Moves Her',
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{'abstract': 'Even conscientiousness dog owners sometimes find themselves in stinky situations.',
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'snippet': 'Even conscientiousness dog owners sometimes find themselves in stinky situations.',
'lead_paragraph': 'Q: When I take my dog for a walk in my suburban New Jersey neighborhood, I try to encourage him to do his business on the berm between the sidewalk and the curb. But on some blocks, lawns run right up to the curb, leaving no sidewalk. A homeowner recently yelled at me for letting my dog go on her lawn, even though I had picked it up. She has since posted a sign telling dogs to stay off her grass. I was taken aback. I’m a courteous dog owner, but what else am I supposed to do? My dog needs to go.',
'print_section': 'RE',
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{'abstract': 'Close contact, confined spaces, crowds, choices — these are the considerations to ponder now.',
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'lead_paragraph': 'When the country was largely under lockdown, at least the rules were mostly clear. Essential workers ventured out; everyone else sheltered in. Bars and restaurants were closed except for dining out; hair salons and spas were shuttered. Outings were limited to the supermarket or the drugstore.',
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{'abstract': 'Shrimp linguine, lemon goop, and more recipes.',
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'lead_paragraph': 'The week’s most popular dishes include Colu Henry’s shrimp linguine with herbs (above), Samantha Seneviratne’s fresh strawberry pie and Ali Slagle’s crispy sour cream and onion chicken. Find more recipes in the collection below.',
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{'abstract': 'Galleries had pushed back, saying coronavirus concerns would prevent visitors from attending the prestigious art gathering.',
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{'abstract': 'Emily Cox and Henry Rathvon send us their puzzles from Delphi.',
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'lead_paragraph': 'ACROSTIC — It’s a shame that you all didn’t see this puzzle when I did, because way back then nobody was talking about White House bunkers and now I find the EAST WING reference supernatural, something out of Nostradamus. Team Cox/Rathvon is always perspicacious, but this is a little unsettling.',
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{'abstract': 'I suppressed my rage about racism for decades. No more.',
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'lead_paragraph': 'I knew we were in trouble when I couldn’t find a way to not be angry, because I had never been angry before, not in a sustained way. It started when Donald Trump was elected. If a black man like me was having trouble corralling his anger, I knew it meant that anger among black people had to have risen to biblical proportions and could ignite given the right spark.',
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{'abstract': 'They’re acting like they exist to protect their endowments, instead of the other way around.',
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'lead_paragraph': 'In 1981, when I was a senior at the University of Michigan, I interviewed the school’s new president, Harold Shapiro. An economist by training, Dr. Shapiro emphasized the importance of increasing the university’s endowment, which then totaled $115 million. That number would nearly triple during his eight-year tenure.',
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{'abstract': 'The Rumble Ponies, a minor-league team in Binghamton, N. Y., employs hundreds of people in the spring and summer. Then came the coronavirus.',
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'headline': {'main': 'How Working From Home Is Working Out (or Not)',
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{'abstract': 'The mayor, Han Kuo-yu, had tried to win the presidency just months ago on a Beijing-friendly platform. His removal in a recall vote reflects a stunning reversal and a hardening of Taiwan’s attitude toward China.',
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{'abstract': 'The original deal would have allowed increases starting next month. The new pact reflects producers’ concerns that the oil market could fall apart again.',
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'lead_paragraph': 'These days, people are turning to art or poetry while stuck at home, because they want to feel something other than being at home. One way to feel something different and exciting is to craft a found poem.',
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{'abstract': 'Catch Michaela Coel’s new drama on HBO. And stream a mockumentary-style comedy about life in a small English village.',
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'lead_paragraph': 'I MAY DESTROY YOU 10:30 p.m. on HBO. Michaela Coel, the British playwright, actress and star of the Netflix series “Chewing Gum,” is back with a new half-hour drama that explores big ideas about dating, exploitation and consent. Coel plays a popular writer named Arabella, who, during an all-night bender with friends, is drugged and assaulted. “The 12-part series alternates between big-picture stories about Arabella’s similarly sexually adventurous peers and some more subjective and disturbing sequences where she reckons with the lingering trauma of what happened to her,” Noel Murray wrote for The New York Times. He added that the series “is not always an easy show to watch, but it should be a conversation-starter.”',
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{'abstract': 'Imperial College aims to develop a vaccine that is cheaper and easier to manufacture and is forming a partnership to sell it in low-income countries and Britain.',
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{'abstract': 'Images from Saturday, June 6, show the scale of the protests against police brutality and racism, following the death of George Floyd.',
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'lead_paragraph': 'Images from Saturday, June 6, show the scale of the protests against police brutality and racism, following the death of George Floyd.',
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{'abstract': 'The Arizona Cardinals star wide receiver writes of present-day unrest and recalls a more peaceful time in his hometown. “We are not listening to one another.”',
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'lead_paragraph': 'The city of Minneapolis taught me about love.',
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{'abstract': 'He became the head of a group that fought tirelessly for the return to Japan of more than a dozen people kidnapped by the North Korean government.',
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{'abstract': 'Cultural institutions are feeling compelled to stand up for racial justice, but artists and activists want action, not just statements of solidarity.',
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'lead_paragraph': 'The apologies keep coming.',
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{'abstract': 'A. G. Sulzberger noted “a significant breakdown in our editing processes” before the publication of an Op-Ed by a United States senator calling for a military response to civic unrest.',
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{'abstract': 'The attorney general’s remarks, which mirrored those of other administration officials, came as the president was scheduled to meet with law enforcement officials at the White House.',
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'major': 'N'},
{'name': 'subject',
'value': 'Black Lives Matter Movement',
'rank': 2,
'major': 'N'},
{'name': 'subject',
'value': 'Demonstrations, Protests and Riots',
'rank': 3,
'major': 'N'},
{'name': 'glocations', 'value': 'United States', 'rank': 4, 'major': 'N'},
{'name': 'subject', 'value': 'Discrimination', 'rank': 5, 'major': 'N'},
{'name': 'subject',
'value': 'Race and Ethnicity',
'rank': 6,
'major': 'N'},
{'name': 'subject',
'value': 'Police Brutality, Misconduct and Shootings',
'rank': 7,
'major': 'N'},
{'name': 'glocations',
'value': 'Los Angeles (Calif)',
'rank': 8,
'major': 'N'},
{'name': 'glocations',
'value': 'Minneapolis (Minn)',
'rank': 9,
'major': 'N'},
{'name': 'glocations',
'value': 'New York City',
'rank': 10,
'major': 'N'},
{'name': 'glocations',
'value': 'Washington (DC)',
'rank': 11,
'major': 'N'}],
'pub_date': '2020-06-08T00:47:35+0000',
'document_type': 'article',
'news_desk': 'National',
'section_name': 'U.S.',
'byline': {'original': 'By Jack Healy and Kim Barker',
'person': [{'firstname': 'Jack',
'middlename': None,
'lastname': 'Healy',
'qualifier': None,
'title': None,
'role': 'reported',
'organization': '',
'rank': 1},
{'firstname': 'Kim',
'middlename': None,
'lastname': 'Barker',
'qualifier': None,
'title': None,
'role': 'reported',
'organization': '',
'rank': 2}],
'organization': None},
'type_of_material': 'News',
'_id': 'nyt://article/abf91257-9294-51e4-a203-e34335680ab7',
'word_count': 1527,
'uri': 'nyt://article/abf91257-9294-51e4-a203-e34335680ab7'},
{'abstract': 'Saying the existing Police Department cannot be reformed, a majority of the City Council has promised to rethink public safety from the ground up in the wake of George Floyd’s killing.',
'web_url': 'https://www.nytimes.com/2020/06/07/us/minneapolis-police-abolish.html',
'snippet': 'Saying the existing Police Department cannot be reformed, a majority of the City Council has promised to rethink public safety from the ground up in the wake of George Floyd’s killing.',
'lead_paragraph': 'MINNEAPOLIS —\xa0Nine members of the Minneapolis City Council —\xa0a veto-proof majority — pledged on Sunday to dismantle the Police Department, promising to create a new system of public safety in a city where law enforcement has long been accused of racism.',
'print_section': 'A',
'print_page': '22',
'source': 'The New York Times',
'multimedia': [{'rank': 0,
'subtype': 'xlarge',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/us/07unrest-mayor-2/merlin_173287572_18faa66d-bb55-43a8-8ee6-47550efc9456-articleLarge.jpg',
'height': 400,
'width': 600,
'subType': 'xlarge',
'crop_name': 'articleLarge',
'legacy': {'xlarge': 'images/2020/06/07/us/07unrest-mayor-2/merlin_173287572_18faa66d-bb55-43a8-8ee6-47550efc9456-articleLarge.jpg',
'xlargewidth': 600,
'xlargeheight': 400}},
{'rank': 0,
'subtype': 'jumbo',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/us/07unrest-mayor-2/merlin_173287572_18faa66d-bb55-43a8-8ee6-47550efc9456-jumbo.jpg',
'height': 683,
'width': 1024,
'subType': 'jumbo',
'crop_name': 'jumbo',
'legacy': {}},
{'rank': 0,
'subtype': 'superJumbo',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/us/07unrest-mayor-2/merlin_173287572_18faa66d-bb55-43a8-8ee6-47550efc9456-superJumbo.jpg',
'height': 1365,
'width': 2048,
'subType': 'superJumbo',
'crop_name': 'superJumbo',
'legacy': {}},
{'rank': 0,
'subtype': 'thumbnail',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/us/07unrest-mayor-2/07unrest-mayor-2-thumbStandard.jpg',
'height': 75,
'width': 75,
'subType': 'thumbnail',
'crop_name': 'thumbStandard',
'legacy': {'thumbnail': 'images/2020/06/07/us/07unrest-mayor-2/07unrest-mayor-2-thumbStandard.jpg',
'thumbnailwidth': 75,
'thumbnailheight': 75}},
{'rank': 0,
'subtype': 'thumbLarge',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/us/07unrest-mayor-2/07unrest-mayor-2-thumbLarge.jpg',
'height': 150,
'width': 150,
'subType': 'thumbLarge',
'crop_name': 'thumbLarge',
'legacy': {}}],
'headline': {'main': 'Minneapolis Will Dismantle Its Police Force, Council Members Pledge',
'kicker': None,
'content_kicker': None,
'print_headline': 'Minneapolis Council Vows To Dismantle and Rethink City’s Police Department',
'name': None,
'seo': None,
'sub': None},
'keywords': [{'name': 'subject',
'value': 'George Floyd Protests (2020)',
'rank': 1,
'major': 'N'},
{'name': 'subject', 'value': 'Police', 'rank': 2, 'major': 'N'},
{'name': 'organizations',
'value': 'Police Department (Minneapolis, Minn)',
'rank': 3,
'major': 'N'},
{'name': 'subject',
'value': 'Black Lives Matter Movement',
'rank': 4,
'major': 'N'},
{'name': 'subject',
'value': 'Police Brutality, Misconduct and Shootings',
'rank': 5,
'major': 'N'},
{'name': 'subject',
'value': 'Demonstrations, Protests and Riots',
'rank': 6,
'major': 'N'},
{'name': 'subject', 'value': 'City Councils', 'rank': 7, 'major': 'N'},
{'name': 'subject',
'value': 'Politics and Government',
'rank': 8,
'major': 'N'},
{'name': 'persons',
'value': 'Frey, Jacob (1981- )',
'rank': 9,
'major': 'N'},
{'name': 'glocations',
'value': 'Minneapolis (Minn)',
'rank': 10,
'major': 'N'},
{'name': 'subject',
'value': 'Budgets and Budgeting',
'rank': 11,
'major': 'N'}],
'pub_date': '2020-06-08T00:56:11+0000',
'document_type': 'article',
'news_desk': 'National',
'section_name': 'U.S.',
'byline': {'original': 'By Dionne Searcey and John Eligon',
'person': [{'firstname': 'Dionne',
'middlename': None,
'lastname': 'Searcey',
'qualifier': None,
'title': None,
'role': 'reported',
'organization': '',
'rank': 1},
{'firstname': 'John',
'middlename': None,
'lastname': 'Eligon',
'qualifier': None,
'title': None,
'role': 'reported',
'organization': '',
'rank': 2}],
'organization': None},
'type_of_material': 'News',
'_id': 'nyt://article/b142ef2e-3b64-54e8-8a80-eb32ef7fcd54',
'word_count': 1483,
'uri': 'nyt://article/b142ef2e-3b64-54e8-8a80-eb32ef7fcd54'},
{'abstract': 'Bobby has already made it, of course. But if there’s money or status involved, he just can’t help himself. And he’s not the only one.',
'web_url': 'https://www.nytimes.com/2020/06/07/arts/television/billions-episode-6-recap.html',
'snippet': 'Bobby has already made it, of course. But if there’s money or status involved, he just can’t help himself. And he’s not the only one.',
'lead_paragraph': 'Fakes, forgeries, phonies, fugazis — they’re all very much on the brain of this week’s crackerjack episode of ‘Billions.’ For some characters, faking it is all they know how to do.',
'source': 'The New York Times',
'multimedia': [{'rank': 0,
'subtype': 'xlarge',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/arts/07billions-recap/07billions-recap-articleLarge.jpg',
'height': 400,
'width': 600,
'subType': 'xlarge',
'crop_name': 'articleLarge',
'legacy': {'xlarge': 'images/2020/06/07/arts/07billions-recap/07billions-recap-articleLarge.jpg',
'xlargewidth': 600,
'xlargeheight': 400}},
{'rank': 0,
'subtype': 'jumbo',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/arts/07billions-recap/07billions-recap-jumbo.jpg',
'height': 683,
'width': 1024,
'subType': 'jumbo',
'crop_name': 'jumbo',
'legacy': {}},
{'rank': 0,
'subtype': 'superJumbo',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/arts/07billions-recap/07billions-recap-superJumbo.jpg',
'height': 800,
'width': 1200,
'subType': 'superJumbo',
'crop_name': 'superJumbo',
'legacy': {}},
{'rank': 0,
'subtype': 'thumbnail',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/arts/07billions-recap/07billions-recap-thumbStandard.jpg',
'height': 75,
'width': 75,
'subType': 'thumbnail',
'crop_name': 'thumbStandard',
'legacy': {'thumbnail': 'images/2020/06/07/arts/07billions-recap/07billions-recap-thumbStandard.jpg',
'thumbnailwidth': 75,
'thumbnailheight': 75}},
{'rank': 0,
'subtype': 'thumbLarge',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/arts/07billions-recap/07billions-recap-thumbLarge.jpg',
'height': 150,
'width': 150,
'subType': 'thumbLarge',
'crop_name': 'thumbLarge',
'legacy': {}}],
'headline': {'main': '‘Billions’ Season 5, Episode 6 Recap: Fake It Till You Make It',
'kicker': 'Billions',
'content_kicker': None,
'print_headline': '',
'name': None,
'seo': None,
'sub': None},
'keywords': [{'name': 'subject',
'value': 'Television',
'rank': 1,
'major': 'N'},
{'name': 'creative_works',
'value': 'Billions (TV Program)',
'rank': 2,
'major': 'N'}],
'pub_date': '2020-06-08T02:00:07+0000',
'document_type': 'article',
'news_desk': 'Culture',
'section_name': 'Arts',
'subsection_name': 'Television',
'byline': {'original': 'By Sean T. Collins',
'person': [{'firstname': 'Sean',
'middlename': 'T.',
'lastname': 'Collins',
'qualifier': None,
'title': None,
'role': 'reported',
'organization': '',
'rank': 1}],
'organization': None},
'type_of_material': 'Review',
'_id': 'nyt://article/b1776788-dba8-5c82-9bda-7d04d3577a29',
'word_count': 1147,
'uri': 'nyt://article/b1776788-dba8-5c82-9bda-7d04d3577a29'},
{'abstract': 'The senator from Utah appeared to be the first Senate Republican to join the demonstrations over the killing of George Floyd, putting himself at odds with the president again.',
'web_url': 'https://www.nytimes.com/2020/06/07/us/politics/mitt-romney-george-floyd-protests.html',
'snippet': 'The senator from Utah appeared to be the first Senate Republican to join the demonstrations over the killing of George Floyd, putting himself at odds with the president again.',
'lead_paragraph': 'WASHINGTON — Senator Mitt Romney of Utah marched with demonstrators toward the White House on Sunday, appearing to be the first Republican senator to join the thousands across the country protesting the death of George Floyd in police custody.',
'print_section': 'A',
'print_page': '21',
'source': 'The New York Times',
'multimedia': [{'rank': 0,
'subtype': 'xlarge',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/08/us/politics/07dc-unrest-romney1/07dc-unrest-romney1-articleLarge.jpg',
'height': 400,
'width': 600,
'subType': 'xlarge',
'crop_name': 'articleLarge',
'legacy': {'xlarge': 'images/2020/06/08/us/politics/07dc-unrest-romney1/07dc-unrest-romney1-articleLarge.jpg',
'xlargewidth': 600,
'xlargeheight': 400}},
{'rank': 0,
'subtype': 'jumbo',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/08/us/politics/07dc-unrest-romney1/07dc-unrest-romney1-jumbo.jpg',
'height': 683,
'width': 1024,
'subType': 'jumbo',
'crop_name': 'jumbo',
'legacy': {}},
{'rank': 0,
'subtype': 'superJumbo',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/08/us/politics/07dc-unrest-romney1/07dc-unrest-romney1-superJumbo.jpg',
'height': 1365,
'width': 2048,
'subType': 'superJumbo',
'crop_name': 'superJumbo',
'legacy': {}},
{'rank': 0,
'subtype': 'thumbnail',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/08/us/politics/07dc-unrest-romney1/07dc-unrest-romney1-thumbStandard.jpg',
'height': 75,
'width': 75,
'subType': 'thumbnail',
'crop_name': 'thumbStandard',
'legacy': {'thumbnail': 'images/2020/06/08/us/politics/07dc-unrest-romney1/07dc-unrest-romney1-thumbStandard.jpg',
'thumbnailwidth': 75,
'thumbnailheight': 75}},
{'rank': 0,
'subtype': 'thumbLarge',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/08/us/politics/07dc-unrest-romney1/07dc-unrest-romney1-thumbLarge.jpg',
'height': 150,
'width': 150,
'subType': 'thumbLarge',
'crop_name': 'thumbLarge',
'legacy': {}}],
'headline': {'main': 'Romney Marches With Protesters in Washington',
'kicker': None,
'content_kicker': None,
'print_headline': 'Romney Joins Protesters in Washington',
'name': None,
'seo': None,
'sub': None},
'keywords': [{'name': 'subject',
'value': 'George Floyd Protests (2020)',
'rank': 1,
'major': 'N'},
{'name': 'subject',
'value': 'Black Lives Matter Movement',
'rank': 2,
'major': 'N'},
{'name': 'subject',
'value': 'Demonstrations, Protests and Riots',
'rank': 3,
'major': 'N'},
{'name': 'subject',
'value': 'Police Brutality, Misconduct and Shootings',
'rank': 4,
'major': 'N'},
{'name': 'subject',
'value': 'United States Politics and Government',
'rank': 5,
'major': 'N'},
{'name': 'organizations',
'value': 'House of Representatives',
'rank': 6,
'major': 'N'},
{'name': 'organizations', 'value': 'Senate', 'rank': 7, 'major': 'N'},
{'name': 'organizations',
'value': 'Republican Party',
'rank': 8,
'major': 'N'},
{'name': 'persons', 'value': 'Romney, Mitt', 'rank': 9, 'major': 'N'},
{'name': 'persons',
'value': 'Romney, George W',
'rank': 10,
'major': 'N'}],
'pub_date': '2020-06-08T02:11:49+0000',
'document_type': 'article',
'news_desk': 'Washington',
'section_name': 'U.S.',
'subsection_name': 'Politics',
'byline': {'original': 'By Emily Cochrane and Aishvarya Kavi',
'person': [{'firstname': 'Emily',
'middlename': None,
'lastname': 'Cochrane',
'qualifier': None,
'title': None,
'role': 'reported',
'organization': '',
'rank': 1},
{'firstname': 'Aishvarya',
'middlename': None,
'lastname': 'Kavi',
'qualifier': None,
'title': None,
'role': 'reported',
'organization': '',
'rank': 2}],
'organization': None},
'type_of_material': 'News',
'_id': 'nyt://article/082ec15c-95bf-549b-b1b7-d063dc55c33a',
'word_count': 773,
'uri': 'nyt://article/082ec15c-95bf-549b-b1b7-d063dc55c33a'},
{'abstract': 'The storm made landfall in southeast Louisiana, causing storm surges, flooding and heavy rain.',
'web_url': 'https://www.nytimes.com/video/us/100000007179208/tropical-storm-cristobal-hits-louisiana-and-florida.html',
'snippet': 'The storm made landfall in southeast Louisiana, causing storm surges, flooding and heavy rain.',
'lead_paragraph': 'The storm made landfall in southeast Louisiana, causing storm surges, flooding and heavy rain.',
'source': 'The New York Times',
'multimedia': [{'rank': 0,
'subtype': 'xlarge',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/multimedia/07xp-cristobal-pix/merlin_173312478_ea869b96-94d0-4509-9c09-0a806ec27102-articleLarge.jpg',
'height': 398,
'width': 600,
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'legacy': {'xlarge': 'images/2020/06/07/multimedia/07xp-cristobal-pix/merlin_173312478_ea869b96-94d0-4509-9c09-0a806ec27102-articleLarge.jpg',
'xlargewidth': 600,
'xlargeheight': 398}},
{'rank': 0,
'subtype': 'jumbo',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/multimedia/07xp-cristobal-pix/merlin_173312478_ea869b96-94d0-4509-9c09-0a806ec27102-jumbo.jpg',
'height': 680,
'width': 1024,
'subType': 'jumbo',
'crop_name': 'jumbo',
'legacy': {}},
{'rank': 0,
'subtype': 'superJumbo',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/multimedia/07xp-cristobal-pix/merlin_173312478_ea869b96-94d0-4509-9c09-0a806ec27102-superJumbo.jpg',
'height': 680,
'width': 1024,
'subType': 'superJumbo',
'crop_name': 'superJumbo',
'legacy': {}},
{'rank': 0,
'subtype': 'thumbnail',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/multimedia/07xp-cristobal-pix/07xp-cristobal-pix-thumbStandard.jpg',
'height': 75,
'width': 75,
'subType': 'thumbnail',
'crop_name': 'thumbStandard',
'legacy': {'thumbnail': 'images/2020/06/07/multimedia/07xp-cristobal-pix/07xp-cristobal-pix-thumbStandard.jpg',
'thumbnailwidth': 75,
'thumbnailheight': 75}},
{'rank': 0,
'subtype': 'thumbLarge',
'caption': None,
'credit': None,
'type': 'image',
'url': 'images/2020/06/07/multimedia/07xp-cristobal-pix/07xp-cristobal-pix-thumbLarge.jpg',
'height': 150,
'width': 150,
'subType': 'thumbLarge',
'crop_name': 'thumbLarge',
'legacy': {}}],
'headline': {'main': 'Tropical Storm Cristobal Hits Louisiana and Florida',
'kicker': None,
'content_kicker': None,
'print_headline': '',
'name': None,
'seo': None,
'sub': None},
'keywords': [{'name': 'subject',
'value': 'Hurricanes and Tropical Storms',
'rank': 1,
'major': 'N'},
{'name': 'subject', 'value': 'Floods', 'rank': 2, 'major': 'N'},
{'name': 'glocations', 'value': 'Florida', 'rank': 3, 'major': 'N'},
{'name': 'glocations', 'value': 'Louisiana', 'rank': 4, 'major': 'N'}],
'pub_date': '2020-06-08T02:21:13+0000',
'document_type': 'multimedia',
'news_desk': '',
'section_name': 'U.S.',
'byline': {'original': 'By Storyful',
'person': [{'firstname': 'Storyful',
'middlename': None,
'lastname': '',
'qualifier': None,
'title': None,
'role': 'reported',
'organization': '',
'rank': 1}],
'organization': None},
'type_of_material': 'Video',
'_id': 'nyt://video/fd275b38-1596-5eb9-8f6f-3c153f467607',
'word_count': 0,
'uri': 'nyt://video/fd275b38-1596-5eb9-8f6f-3c153f467607'},
...],
'meta': {'hits': 4492}}}
We have a succesful request. Now let;s put it into a dataframe, which can be readily done using the json nesting method I've been using.
nyt_df = pd.DataFrame(r['response']['docs'])
nyt_df
| abstract | web_url | snippet | lead_paragraph | source | multimedia | headline | keywords | pub_date | document_type | news_desk | section_name | byline | type_of_material | _id | word_count | uri | print_section | print_page | subsection_name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | So far, 47 police vehicles have been damaged. ... | https://www.nytimes.com/2020/05/31/nyregion/po... | So far, 47 police vehicles have been damaged. ... | On Friday night in the Fort Greene section of ... | The New York Times | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | {'main': 'Symbol of N.Y.C. Unrest: A Burning P... | [{'name': 'subject', 'value': 'Demonstrations,... | 2020-06-01T00:07:56+0000 | article | Metro | New York | {'original': 'By Azi Paybarah and Nikita Stewa... | News | nyt://article/3022efc6-c3a7-538e-9c33-9396be9a... | 808 | nyt://article/3022efc6-c3a7-538e-9c33-9396be9a... | NaN | NaN | NaN |
| 1 | The death of George Floyd at the hands of the ... | https://www.nytimes.com/video/us/1000000071654... | The death of George Floyd at the hands of the ... | The death of George Floyd at the hands of the ... | The New York Times | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | {'main': '‘We’re Sick and Tired’: Voices From ... | [{'name': 'persons', 'value': 'Floyd, George (... | 2020-06-01T00:08:23+0000 | multimedia | U.S. | {'original': 'By Katie G. Nelson, Mike Shum, S... | Video | nyt://video/d426a47f-812b-5fee-b0e0-75733d2e8543 | 0 | nyt://video/d426a47f-812b-5fee-b0e0-75733d2e8543 | NaN | NaN | NaN | |
| 2 | The truck can be seen speeding toward proteste... | https://www.nytimes.com/video/us/1000000071663... | The truck can be seen speeding toward proteste... | The truck can be seen speeding toward proteste... | The New York Times | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | {'main': 'Video Appears to Show a Tanker Truck... | [{'name': 'subject', 'value': 'Demonstrations,... | 2020-06-01T00:22:27+0000 | multimedia | U.S. | {'original': 'By The New York Times', 'person'... | Video | nyt://video/2c42036b-8ef6-5ba5-8375-a85cb7deda40 | 0 | nyt://video/2c42036b-8ef6-5ba5-8375-a85cb7deda40 | NaN | NaN | NaN | |
| 3 | In communities across the country, congregatio... | https://www.nytimes.com/2020/05/31/us/churches... | In communities across the country, congregatio... | SACRAMENTO — With their most vulnerable member... | The New York Times | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | {'main': 'Amid Riots and a Pandemic, Church At... | [{'name': 'subject', 'value': 'Churches (Build... | 2020-06-01T00:28:09+0000 | article | National | U.S. | {'original': 'By Shawn Hubler and Rick Rojas',... | News | nyt://article/eeb31381-63db-5bf0-8e56-c92b1bc2... | 1524 | nyt://article/eeb31381-63db-5bf0-8e56-c92b1bc2... | A | 14 | NaN |
| 4 | Videos showed officers using batons, tear gas,... | https://www.nytimes.com/2020/05/31/us/police-t... | Videos showed officers using batons, tear gas,... | [Follow our live updates on the George Floyd p... | The New York Times | [] | {'main': 'Facing Protests Over Use of Force, P... | [{'name': 'subject', 'value': 'George Floyd Pr... | 2020-06-01T00:45:13+0000 | article | National | U.S. | {'original': 'By Shaila Dewan and Mike Baker',... | News | nyt://article/25642b46-e875-56a1-b7fd-1be7c34a... | 1539 | nyt://article/25642b46-e875-56a1-b7fd-1be7c34a... | A | 1 | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4487 | https://www.nytimes.com/interactive/2020/06/30... | The New York Times | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | {'main': 'Oklahoma Primary Election Results: F... | [{'name': 'subject', 'value': 'Elections', 'ra... | 2020-06-30T23:50:20+0000 | multimedia | U.S. | U.S. | {'original': '', 'person': [], 'organization':... | nyt://interactive/d954de2f-d0d3-5b03-a828-cb21... | 0 | nyt://interactive/d954de2f-d0d3-5b03-a828-cb21... | NaN | NaN | Elections | ||||
| 4488 | https://www.nytimes.com/interactive/2020/06/30... | The New York Times | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | {'main': 'Colorado Primary Election Results: T... | [{'name': 'subject', 'value': 'Elections', 'ra... | 2020-06-30T23:50:20+0000 | multimedia | U.S. | U.S. | {'original': '', 'person': [], 'organization':... | nyt://interactive/f486d28b-555d-52ec-b069-fe5e... | 0 | nyt://interactive/f486d28b-555d-52ec-b069-fe5e... | NaN | NaN | Elections | ||||
| 4489 | https://www.nytimes.com/interactive/2020/06/30... | The New York Times | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | {'main': 'Oklahoma U.S. Senate Primary Electio... | [{'name': 'subject', 'value': 'Elections', 'ra... | 2020-06-30T23:50:20+0000 | multimedia | U.S. | U.S. | {'original': '', 'person': [], 'organization':... | nyt://interactive/f501e742-e85e-52b6-bc00-ca8c... | 0 | nyt://interactive/f501e742-e85e-52b6-bc00-ca8c... | NaN | NaN | Elections | ||||
| 4490 | https://www.nytimes.com/interactive/2020/06/30... | The New York Times | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | {'main': 'Oklahoma Primary Election Results: F... | [{'name': 'subject', 'value': 'Elections', 'ra... | 2020-06-30T23:50:20+0000 | multimedia | U.S. | U.S. | {'original': '', 'person': [], 'organization':... | nyt://interactive/fb8ae641-5cb6-552e-9be4-d73a... | 0 | nyt://interactive/fb8ae641-5cb6-552e-9be4-d73a... | NaN | NaN | Elections | ||||
| 4491 | This file was published in error | https://www.nytimes.com/2020/06/30/theater/thi... | This file was published in error | This file was published in error | The New York Times | [{'rank': 0, 'subtype': 'xlarge', 'caption': N... | {'main': 'This file was published in error', '... | [{'name': 'subject', 'value': 'Theater', 'rank... | 2020-06-30T23:54:48+0000 | article | Culture | Theater | {'original': '', 'person': [], 'organization':... | News | nyt://article/7fc5a07f-d62c-523f-a1f1-8152bf02... | 6 | nyt://article/7fc5a07f-d62c-523f-a1f1-8152bf02... | NaN | NaN | NaN |
4492 rows × 20 columns
There is plenty of data cleanup that I can do on this dataset but that will be the subject of a different post.
Next, I'll search for movie reviews on The Matrix Trilogy that were posted from March 1999 until December 2003. The Matrix Trilogy movies were released on March 1999, May 2003, and December 2003.
api_key = 'jhkMYAlxMsPPYrLatKFLwF6JbQLhME9V'
movie_title = 'matrix'
start_date = '1999-03-01'
end_date = '2003-12-31'
base_url = 'https://api.nytimes.com/svc/movies/v2/reviews/search.json?query='
search_url = base_url + f'{movie_title}&api-key={api_key}&opening-date={start_date}:{end_date}'
print(search_url)
r = requests.get(search_url)
r = r.json()
r
https://api.nytimes.com/svc/movies/v2/reviews/search.json?query=matrix&api-key=jhkMYAlxMsPPYrLatKFLwF6JbQLhME9V&opening-date=1999-03-01:2003-12-31
{'status': 'OK',
'copyright': 'Copyright (c) 2022 The New York Times Company. All Rights Reserved.',
'has_more': False,
'num_results': 3,
'results': [{'display_title': 'The Matrix Revolutions',
'mpaa_rating': 'R',
'critics_pick': 0,
'byline': 'A. O. SCOTT',
'headline': 'The Game Concludes With Light and Noise',
'summary_short': '"Everything that has a beginning has an end." Yes, thank God — or Neo, or the One, or the Source, or the Oracle, or the Architect, or whoever it was who set this metaphysical action trilogy in motion. "Reloaded," the second installment, was overstuffed and ungainly; this concluding chapter feels padded, with a long, grinding battle sequence in the middle and a whole lot of mumbo-jumbo at the end. Keanu Reeves, perhaps worried that he was showing too much range, has purged himself of all expression apart from a worried frown and a sorrowful grimace, which is mirrored in the face of Carrie-Anne Moss, who returns as Trinity. The salient distinction of the human remnants in Zion, apart from their earth-toned natural-fiber fashions, has been the capacity for love, a word much spoken in this movie. But their ranks, in spite of the presence of fine actors like Laurence Fishburne, Harry Lennix and Harold Perrineau, become more robotic with every passing scene. The sole exception is Jada Pinkett Smith as the daredevil pilot Niobe, who brings a touch of bad-girl B-movie attitude into the humorless subterranean gloom. It is not altogether worthless: the Wachowski Brothers still know how to stage an action sequence, and there are some scenes that have a quiet, mysterious beauty. If they would only let go of their grandiose theological pretensions, they might once again be interesting filmmakers. — A. O. Scott',
'publication_date': '2003-11-05',
'opening_date': '2003-11-05',
'date_updated': '2017-11-02 04:18:03',
'link': {'type': 'article',
'url': 'https://www.nytimes.com/2003/11/05/movies/film-review-the-game-concludes-with-light-and-noise.html',
'suggested_link_text': 'Read the New York Times Review of The Matrix Revolutions'},
'multimedia': None},
{'display_title': 'The Matrix Reloaded',
'mpaa_rating': 'R',
'critics_pick': 0,
'byline': 'Elvis Mitchell',
'headline': 'An Idealized World and a Troubled Hero',
'summary_short': "The mastery of filmmaking hasn't abated in "Reloaded" — the Wachowskis are as suited to the task of guiding this sequel to their surprise 1999 hit as their unsure hero, Neo (Keanu Reeves), is to saving the world. Though the staging of the action sequences is as viciously elegant as you've been primed to expect, there's a dispiriting more-of-the-same aspect to the picture. The mentor Morpheus (Laurence Fishburne) and the virtual villain Agent Smith (Hugo Weaving) also return, and a whole new group of characters surface as Neo, his lover, Trinity (Carrie-Anne Moss), and the crew continue to fight the oppressive forces threatening to eliminate humanity and the hidden city of Zion. As the middle film of a trilogy, "Reloaded" seeks to increase the emotional stakes — which it doesn't quite accomplish — while leaving enough of a cliffhanger that audiences will be bedeviled enough to flock back into the tents for the last portion. The directors pay their audiences the unlikely compliment of taking them seriously. Unfortunately, they take themselves too seriously. However, "Reloaded" has one of the most excitingly subversive and radical points of view ever seen in a major motion picture — a postmodern purview that accords philosophical ideals from people of color equal weight. This includes the warmhearted spotlight given to Gloria Foster, who returns as Oracle, the source of all knowledge for Neo. — Elvis Mitchell",
'publication_date': '2003-05-14',
'opening_date': '2003-05-15',
'date_updated': '2017-11-02 04:18:02',
'link': {'type': 'article',
'url': 'https://www.nytimes.com/2003/05/14/movies/film-review-an-idealized-world-and-a-troubled-hero.html',
'suggested_link_text': 'Read the New York Times Review of The Matrix Reloaded'},
'multimedia': None},
{'display_title': 'The Matrix',
'mpaa_rating': 'R',
'critics_pick': 0,
'byline': 'Janet Maslin',
'headline': 'Matrix, The (Movie) ',
'summary_short': 'Hacker as action messiah. Enough visual bravado to sustain steady suspense. Cult classic, virtually speaking.',
'publication_date': '1999-03-31',
'opening_date': '1999-03-31',
'date_updated': '2017-11-02 04:17:57',
'link': {'type': 'article',
'url': 'https://www.nytimes.com/1999/03/31/movies/film-review-the-reality-is-all-virtual-and-densely-complicated.html',
'suggested_link_text': 'Read the New York Times Review of The Matrix'},
'multimedia': None}]}
matrix_reviews_df = pd.DataFrame(r['results'])
matrix_reviews_df
| display_title | mpaa_rating | critics_pick | byline | headline | summary_short | publication_date | opening_date | date_updated | link | multimedia | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | The Matrix Revolutions | R | 0 | A. O. SCOTT | The Game Concludes With Light and Noise | "Everything that has a beginning has an e... | 2003-11-05 | 2003-11-05 | 2017-11-02 04:18:03 | {'type': 'article', 'url': 'https://www.nytime... | None |
| 1 | The Matrix Reloaded | R | 0 | Elvis Mitchell | An Idealized World and a Troubled Hero | The mastery of filmmaking hasn't abated in &qu... | 2003-05-14 | 2003-05-15 | 2017-11-02 04:18:02 | {'type': 'article', 'url': 'https://www.nytime... | None |
| 2 | The Matrix | R | 0 | Janet Maslin | Matrix, The (Movie) | Hacker as action messiah. Enough visual bravad... | 1999-03-31 | 1999-03-31 | 2017-11-02 04:17:57 | {'type': 'article', 'url': 'https://www.nytime... | None |
Sweet! I could start generating a database on movie reviews to do things like sentiment analysis for a future project :]
So anyone that knows me even a little knows that I LOVE MEMES. So it is only natural that at some point or another I'd be interested in making my own meme routine. imgFlip was a nice API that allows you to get memes in a pretty easy way. You can find info on the ImgFlip API here: https://imgflip.com/api.
Let's start by requesting a json with the 100 meme templates that were most captioned over the last 30 days.
url = "https://api.imgflip.com/get_memes"
memes = requests.get(url)
meme_dict = memes.json()
meme_dict
{'success': True,
'data': {'memes': [{'id': '181913649',
'name': 'Drake Hotline Bling',
'url': 'https://i.imgflip.com/30b1gx.jpg',
'width': 1200,
'height': 1200,
'box_count': 2},
{'id': '87743020',
'name': 'Two Buttons',
'url': 'https://i.imgflip.com/1g8my4.jpg',
'width': 600,
'height': 908,
'box_count': 3},
{'id': '112126428',
'name': 'Distracted Boyfriend',
'url': 'https://i.imgflip.com/1ur9b0.jpg',
'width': 1200,
'height': 800,
'box_count': 3},
{'id': '131087935',
'name': 'Running Away Balloon',
'url': 'https://i.imgflip.com/261o3j.jpg',
'width': 761,
'height': 1024,
'box_count': 5},
{'id': '129242436',
'name': 'Change My Mind',
'url': 'https://i.imgflip.com/24y43o.jpg',
'width': 482,
'height': 361,
'box_count': 2},
{'id': '217743513',
'name': 'UNO Draw 25 Cards',
'url': 'https://i.imgflip.com/3lmzyx.jpg',
'width': 500,
'height': 494,
'box_count': 2},
{'id': '124822590',
'name': 'Left Exit 12 Off Ramp',
'url': 'https://i.imgflip.com/22bdq6.jpg',
'width': 804,
'height': 767,
'box_count': 3},
{'id': '247375501',
'name': 'Buff Doge vs. Cheems',
'url': 'https://i.imgflip.com/43a45p.png',
'width': 937,
'height': 720,
'box_count': 4},
{'id': '222403160',
'name': 'Bernie I Am Once Again Asking For Your Support',
'url': 'https://i.imgflip.com/3oevdk.jpg',
'width': 750,
'height': 750,
'box_count': 2},
{'id': '131940431',
'name': "Gru's Plan",
'url': 'https://i.imgflip.com/26jxvz.jpg',
'width': 700,
'height': 449,
'box_count': 4},
{'id': '4087833',
'name': 'Waiting Skeleton',
'url': 'https://i.imgflip.com/2fm6x.jpg',
'width': 298,
'height': 403,
'box_count': 2},
{'id': '135256802',
'name': 'Epic Handshake',
'url': 'https://i.imgflip.com/28j0te.jpg',
'width': 900,
'height': 645,
'box_count': 3},
{'id': '80707627',
'name': 'Sad Pablo Escobar',
'url': 'https://i.imgflip.com/1c1uej.jpg',
'width': 720,
'height': 709,
'box_count': 3},
{'id': '438680',
'name': 'Batman Slapping Robin',
'url': 'https://i.imgflip.com/9ehk.jpg',
'width': 400,
'height': 387,
'box_count': 2},
{'id': '93895088',
'name': 'Expanding Brain',
'url': 'https://i.imgflip.com/1jwhww.jpg',
'width': 857,
'height': 1202,
'box_count': 4},
{'id': '252600902',
'name': 'Always Has Been',
'url': 'https://i.imgflip.com/46e43q.png',
'width': 960,
'height': 540,
'box_count': 2},
{'id': '188390779',
'name': 'Woman Yelling At Cat',
'url': 'https://i.imgflip.com/345v97.jpg',
'width': 680,
'height': 438,
'box_count': 2},
{'id': '97984',
'name': 'Disaster Girl',
'url': 'https://i.imgflip.com/23ls.jpg',
'width': 500,
'height': 375,
'box_count': 2},
{'id': '178591752',
'name': 'Tuxedo Winnie The Pooh',
'url': 'https://i.imgflip.com/2ybua0.png',
'width': 800,
'height': 582,
'box_count': 2},
{'id': '110163934',
'name': "I Bet He's Thinking About Other Women",
'url': 'https://i.imgflip.com/1tl71a.jpg',
'width': 1654,
'height': 930,
'box_count': 2},
{'id': '1035805',
'name': 'Boardroom Meeting Suggestion',
'url': 'https://i.imgflip.com/m78d.jpg',
'width': 500,
'height': 649,
'box_count': 4},
{'id': '102156234',
'name': 'Mocking Spongebob',
'url': 'https://i.imgflip.com/1otk96.jpg',
'width': 502,
'height': 353,
'box_count': 2},
{'id': '91538330',
'name': 'X, X Everywhere',
'url': 'https://i.imgflip.com/1ihzfe.jpg',
'width': 2118,
'height': 1440,
'box_count': 2},
{'id': '79132341',
'name': 'Bike Fall',
'url': 'https://i.imgflip.com/1b42wl.jpg',
'width': 500,
'height': 680,
'box_count': 3},
{'id': '119139145',
'name': 'Blank Nut Button',
'url': 'https://i.imgflip.com/1yxkcp.jpg',
'width': 600,
'height': 446,
'box_count': 2},
{'id': '101470',
'name': 'Ancient Aliens',
'url': 'https://i.imgflip.com/26am.jpg',
'width': 500,
'height': 437,
'box_count': 2},
{'id': '61579',
'name': 'One Does Not Simply',
'url': 'https://i.imgflip.com/1bij.jpg',
'width': 568,
'height': 335,
'box_count': 2},
{'id': '195515965',
'name': 'Clown Applying Makeup',
'url': 'https://i.imgflip.com/38el31.jpg',
'width': 750,
'height': 798,
'box_count': 4},
{'id': '27813981',
'name': 'Hide the Pain Harold',
'url': 'https://i.imgflip.com/gk5el.jpg',
'width': 480,
'height': 601,
'box_count': 2},
{'id': '216951317',
'name': 'Guy Holding Cardboard Sign',
'url': 'https://i.imgflip.com/3l60ph.jpg',
'width': 700,
'height': 702,
'box_count': 2},
{'id': '180190441',
'name': "They're The Same Picture",
'url': 'https://i.imgflip.com/2za3u1.jpg',
'width': 1363,
'height': 1524,
'box_count': 3},
{'id': '148909805',
'name': 'Monkey Puppet',
'url': 'https://i.imgflip.com/2gnnjh.jpg',
'width': 923,
'height': 768,
'box_count': 2},
{'id': '100777631',
'name': 'Is This A Pigeon',
'url': 'https://i.imgflip.com/1o00in.jpg',
'width': 1587,
'height': 1425,
'box_count': 3},
{'id': '161865971',
'name': 'Marked Safe From',
'url': 'https://i.imgflip.com/2odckz.jpg',
'width': 618,
'height': 499,
'box_count': 2},
{'id': '226297822',
'name': 'Panik Kalm Panik',
'url': 'https://i.imgflip.com/3qqcim.png',
'width': 640,
'height': 881,
'box_count': 3},
{'id': '114585149',
'name': 'Inhaling Seagull',
'url': 'https://i.imgflip.com/1w7ygt.jpg',
'width': 1269,
'height': 2825,
'box_count': 4},
{'id': '55311130',
'name': 'This Is Fine',
'url': 'https://i.imgflip.com/wxica.jpg',
'width': 580,
'height': 282,
'box_count': 2},
{'id': '124055727',
'name': "Y'all Got Any More Of That",
'url': 'https://i.imgflip.com/21uy0f.jpg',
'width': 600,
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'box_count': 2},
{'id': '3218037',
'name': "This Is Where I'd Put My Trophy If I Had One",
'url': 'https://i.imgflip.com/1wz1x.jpg',
'width': 300,
'height': 418,
'box_count': 2},
{'id': '89370399',
'name': 'Roll Safe Think About It',
'url': 'https://i.imgflip.com/1h7in3.jpg',
'width': 702,
'height': 395,
'box_count': 2},
{'id': '134797956',
'name': 'American Chopper Argument',
'url': 'https://i.imgflip.com/2896ro.jpg',
'width': 640,
'height': 1800,
'box_count': 5},
{'id': '123999232',
'name': 'The Scroll Of Truth',
'url': 'https://i.imgflip.com/21tqf4.jpg',
'width': 1280,
'height': 1236,
'box_count': 2},
{'id': '135678846',
'name': 'Who Killed Hannibal',
'url': 'https://i.imgflip.com/28s2gu.jpg',
'width': 1280,
'height': 1440,
'box_count': 3},
{'id': '99683372',
'name': 'Sleeping Shaq',
'url': 'https://i.imgflip.com/1nck6k.jpg',
'width': 640,
'height': 631,
'box_count': 2},
{'id': '28251713',
'name': 'Oprah You Get A',
'url': 'https://i.imgflip.com/gtj5t.jpg',
'width': 620,
'height': 465,
'box_count': 2},
{'id': '21735',
'name': 'The Rock Driving',
'url': 'https://i.imgflip.com/grr.jpg',
'width': 568,
'height': 700,
'box_count': 2},
{'id': '155067746',
'name': 'Surprised Pikachu',
'url': 'https://i.imgflip.com/2kbn1e.jpg',
'width': 1893,
'height': 1893,
'box_count': 3},
{'id': '259237855',
'name': 'Laughing Leo',
'url': 'https://i.imgflip.com/4acd7j.png',
'width': 470,
'height': 470,
'box_count': 2},
{'id': '6235864',
'name': 'Finding Neverland',
'url': 'https://i.imgflip.com/3pnmg.jpg',
'width': 423,
'height': 600,
'box_count': 3},
{'id': '61556',
'name': 'Grandma Finds The Internet',
'url': 'https://i.imgflip.com/1bhw.jpg',
'width': 640,
'height': 480,
'box_count': 2},
{'id': '132769734',
'name': 'Hard To Swallow Pills',
'url': 'https://i.imgflip.com/271ps6.jpg',
'width': 680,
'height': 979,
'box_count': 2},
{'id': '61520',
'name': 'Futurama Fry',
'url': 'https://i.imgflip.com/1bgw.jpg',
'width': 552,
'height': 414,
'box_count': 2},
{'id': '84341851',
'name': 'Evil Kermit',
'url': 'https://i.imgflip.com/1e7ql7.jpg',
'width': 700,
'height': 325,
'box_count': 2},
{'id': '175540452',
'name': 'Unsettled Tom',
'url': 'https://i.imgflip.com/2wifvo.jpg',
'width': 680,
'height': 550,
'box_count': 2},
{'id': '101288',
'name': 'Third World Skeptical Kid',
'url': 'https://i.imgflip.com/265k.jpg',
'width': 426,
'height': 426,
'box_count': 2},
{'id': '5496396',
'name': 'Leonardo Dicaprio Cheers',
'url': 'https://i.imgflip.com/39t1o.jpg',
'width': 600,
'height': 400,
'box_count': 2},
{'id': '196652226',
'name': 'Spongebob Ight Imma Head Out',
'url': 'https://i.imgflip.com/392xtu.jpg',
'width': 822,
'height': 960,
'box_count': 2},
{'id': '91545132',
'name': 'Trump Bill Signing',
'url': 'https://i.imgflip.com/1ii4oc.jpg',
'width': 1866,
'height': 1529,
'box_count': 2},
{'id': '8072285',
'name': 'Doge',
'url': 'https://i.imgflip.com/4t0m5.jpg',
'width': 620,
'height': 620,
'box_count': 5},
{'id': '61544',
'name': 'Success Kid',
'url': 'https://i.imgflip.com/1bhk.jpg',
'width': 500,
'height': 500,
'box_count': 2},
{'id': '61532',
'name': 'The Most Interesting Man In The World',
'url': 'https://i.imgflip.com/1bh8.jpg',
'width': 550,
'height': 690,
'box_count': 2},
{'id': '563423',
'name': 'That Would Be Great',
'url': 'https://i.imgflip.com/c2qn.jpg',
'width': 526,
'height': 440,
'box_count': 2},
{'id': '14371066',
'name': 'Star Wars Yoda',
'url': 'https://i.imgflip.com/8k0sa.jpg',
'width': 620,
'height': 714,
'box_count': 2},
{'id': '29617627',
'name': 'Look At Me',
'url': 'https://i.imgflip.com/hmt3v.jpg',
'width': 300,
'height': 300,
'box_count': 2},
{'id': '61546',
'name': 'Brace Yourselves X is Coming',
'url': 'https://i.imgflip.com/1bhm.jpg',
'width': 622,
'height': 477,
'box_count': 2},
{'id': '101716',
'name': 'Yo Dawg Heard You',
'url': 'https://i.imgflip.com/26hg.jpg',
'width': 500,
'height': 323,
'box_count': 2},
{'id': '163573',
'name': 'Imagination Spongebob',
'url': 'https://i.imgflip.com/3i7p.jpg',
'width': 500,
'height': 366,
'box_count': 2},
{'id': '460541',
'name': 'Jack Sparrow Being Chased',
'url': 'https://i.imgflip.com/9vct.jpg',
'width': 500,
'height': 375,
'box_count': 2},
{'id': '61539',
'name': 'First World Problems',
'url': 'https://i.imgflip.com/1bhf.jpg',
'width': 552,
'height': 367,
'box_count': 2},
{'id': '1367068',
'name': 'I Should Buy A Boat Cat',
'url': 'https://i.imgflip.com/tau4.jpg',
'width': 500,
'height': 368,
'box_count': 2},
{'id': '24557067',
'name': 'Afraid To Ask Andy',
'url': 'https://i.imgflip.com/emccr.jpg',
'width': 620,
'height': 608,
'box_count': 2},
{'id': '6531067',
'name': 'See Nobody Cares',
'url': 'https://i.imgflip.com/3vzej.jpg',
'width': 620,
'height': 676,
'box_count': 2},
{'id': '285870',
'name': 'Squidward',
'url': 'https://i.imgflip.com/64ku.jpg',
'width': 500,
'height': 750,
'box_count': 2},
{'id': '61580',
'name': 'Too Damn High',
'url': 'https://i.imgflip.com/1bik.jpg',
'width': 420,
'height': 316,
'box_count': 2},
{'id': '142921050',
'name': 'Car Salesman Slaps Roof Of Car',
'url': 'https://i.imgflip.com/2d3al6.jpg',
'width': 800,
'height': 450,
'box_count': 2},
{'id': '29562797',
'name': "I'm The Captain Now",
'url': 'https://i.imgflip.com/hlmst.jpg',
'width': 478,
'height': 350,
'box_count': 2},
{'id': '61585',
'name': 'Bad Luck Brian',
'url': 'https://i.imgflip.com/1bip.jpg',
'width': 475,
'height': 562,
'box_count': 2},
{'id': '21604248',
'name': 'Mugatu So Hot Right Now',
'url': 'https://i.imgflip.com/cv1y0.jpg',
'width': 620,
'height': 497,
'box_count': 2},
{'id': '101511',
'name': "Don't You Squidward",
'url': 'https://i.imgflip.com/26br.jpg',
'width': 500,
'height': 333,
'box_count': 2},
{'id': '61533',
'name': 'X All The Y',
'url': 'https://i.imgflip.com/1bh9.jpg',
'width': 500,
'height': 355,
'box_count': 2},
{'id': '922147',
'name': 'Laughing Men In Suits',
'url': 'https://i.imgflip.com/jrj7.jpg',
'width': 500,
'height': 333,
'box_count': 2},
{'id': '8279814',
'name': 'Cute Cat',
'url': 'https://i.imgflip.com/4xgqu.jpg',
'width': 480,
'height': 532,
'box_count': 2},
{'id': '56225174',
'name': 'Be Like Bill',
'url': 'https://i.imgflip.com/xh3me.jpg',
'width': 913,
'height': 907,
'box_count': 4},
{'id': '7183956',
'name': 'Oprah You Get A Car Everybody Gets A Car',
'url': 'https://i.imgflip.com/49z6c.jpg',
'width': 620,
'height': 1004,
'box_count': 4},
{'id': '101910402',
'name': 'Who Would Win?',
'url': 'https://i.imgflip.com/1ooaki.jpg',
'width': 802,
'height': 500,
'box_count': 2},
{'id': '183518946',
'name': 'Blank Transparent Square',
'url': 'https://i.imgflip.com/319g4i.png',
'width': 1000,
'height': 1000,
'box_count': 2},
{'id': '61527',
'name': 'Y U No',
'url': 'https://i.imgflip.com/1bh3.jpg',
'width': 500,
'height': 500,
'box_count': 2},
{'id': '1464444',
'name': 'Happy Star Congratulations',
'url': 'https://i.imgflip.com/vdz0.jpg',
'width': 450,
'height': 292,
'box_count': 4},
{'id': '89655',
'name': 'Uncle Sam',
'url': 'https://i.imgflip.com/1x6f.jpg',
'width': 620,
'height': 833,
'box_count': 2},
{'id': '176908',
'name': 'Shut Up And Take My Money Fry',
'url': 'https://i.imgflip.com/3si4.jpg',
'width': 500,
'height': 281,
'box_count': 2},
{'id': '1202623',
'name': 'Keep Calm And Carry On Red',
'url': 'https://i.imgflip.com/pry7.jpg',
'width': 500,
'height': 704,
'box_count': 2},
{'id': '101287',
'name': 'Third World Success Kid',
'url': 'https://i.imgflip.com/265j.jpg',
'width': 500,
'height': 500,
'box_count': 2},
{'id': '16464531',
'name': "But That's None Of My Business",
'url': 'https://i.imgflip.com/9sw43.jpg',
'width': 600,
'height': 600,
'box_count': 2},
{'id': '71428573',
'name': 'Say it Again, Dexter',
'url': 'https://i.imgflip.com/16iyn1.jpg',
'width': 698,
'height': 900,
'box_count': 2},
{'id': '61516',
'name': 'Philosoraptor',
'url': 'https://i.imgflip.com/1bgs.jpg',
'width': 500,
'height': 500,
'box_count': 2},
{'id': '371382',
'name': 'Simba Shadowy Place',
'url': 'https://i.imgflip.com/7yk6.jpg',
'width': 363,
'height': 720,
'box_count': 2},
{'id': '28034788',
'name': 'Marvel Civil War 1',
'url': 'https://i.imgflip.com/govs4.jpg',
'width': 423,
'height': 734,
'box_count': 2},
{'id': '405658',
'name': 'Grumpy Cat',
'url': 'https://i.imgflip.com/8p0a.jpg',
'width': 500,
'height': 617,
'box_count': 2},
{'id': '61581',
'name': 'Put It Somewhere Else Patrick',
'url': 'https://i.imgflip.com/1bil.jpg',
'width': 343,
'height': 604,
'box_count': 2},
{'id': '109765',
'name': "I'll Just Wait Here",
'url': 'https://i.imgflip.com/2cp1.jpg',
'width': 491,
'height': 550,
'box_count': 2}]}}
Another, nested json. No worries though, we know how to deal with them. Let's put these results into a dataframe
meme_df = pd.DataFrame(meme_dict['data']['memes'])
meme_df
| id | name | url | width | height | box_count | |
|---|---|---|---|---|---|---|
| 0 | 181913649 | Drake Hotline Bling | https://i.imgflip.com/30b1gx.jpg | 1200 | 1200 | 2 |
| 1 | 87743020 | Two Buttons | https://i.imgflip.com/1g8my4.jpg | 600 | 908 | 3 |
| 2 | 112126428 | Distracted Boyfriend | https://i.imgflip.com/1ur9b0.jpg | 1200 | 800 | 3 |
| 3 | 131087935 | Running Away Balloon | https://i.imgflip.com/261o3j.jpg | 761 | 1024 | 5 |
| 4 | 129242436 | Change My Mind | https://i.imgflip.com/24y43o.jpg | 482 | 361 | 2 |
| ... | ... | ... | ... | ... | ... | ... |
| 95 | 371382 | Simba Shadowy Place | https://i.imgflip.com/7yk6.jpg | 363 | 720 | 2 |
| 96 | 28034788 | Marvel Civil War 1 | https://i.imgflip.com/govs4.jpg | 423 | 734 | 2 |
| 97 | 405658 | Grumpy Cat | https://i.imgflip.com/8p0a.jpg | 500 | 617 | 2 |
| 98 | 61581 | Put It Somewhere Else Patrick | https://i.imgflip.com/1bil.jpg | 343 | 604 | 2 |
| 99 | 109765 | I'll Just Wait Here | https://i.imgflip.com/2cp1.jpg | 491 | 550 | 2 |
100 rows × 6 columns
Cool beans! Now we can do stuff with them. I'll take the url column and then convert it into a list. Then, I'll display the first 10 memes in the dataframe:
from PIL import Image #I'm using PIL because the urlopen method I was using with OpenCV
#was giving me a HTTP 403 error
meme_url = meme_df['url'].tolist()
fig, axs = plt.subplots(1, 10, figsize=(30,5))
i = 0
for meme in meme_url:
im = Image.open(requests.get(meme, stream=True).raw)
axs[i].imshow(im)
i+=1
if i > 9:
break
plt.show()
Cool! Now we can download meme templates at our hearts content! I have some ideas for projects with this :]
praw wrapper function for the Reddit API to facilitate operations.
import praw #This is to access Reddit data
cid = 'PGDDWn6zsy1RBZ6E3ExWsA'
secret = 'AmtIRyYfqGF4ak430RIyU83GUK5ymg'
username = 'anisotropy55'
user_agent = "anisotropy55"
password = "YY7MJnCMgKsZcxU"
#This is for read only operations
reddit = praw.Reddit(client_id = cid,client_secret = secret,user_agent = username,)
print(reddit.read_only)
True
Let's start by getting the top 10 posts in r/programming
for submission in reddit.subreddit("programming").hot(limit=10):
print(submission.title)
We need young programmers; we need old programmers After two years of self-studying programming, I made an AI-powered essay writing tool/website that helps you organize ideas and craft better arguments Coming Soon: Reddit Developer Platform I have complicated feelings about TDD Agile Projects Have Become Waterfall Projects With Sprints Removing HTTP/2 Server Push from Chrome This IBM security article will implement a simple authentication mechanism to demonstrate how developers can write their own Java based authentication mechanism. Browser extension Surfingkeys, which implements vim-style shortcuts in browsers, appears to be bundling a search hijacker. This does not appear in source Julia 1.8 Teams, Zoom and Meeting Overload Are Slowing Down Development and Disrupting Developers
If we navigate to r/programming then we'll see that our 10 produced posts match what is actually posted. Great!
Now I'll see if I can make something a bit more involved by making an Authorized Reddit instance. This would enable me to use my code in whatever I, as a human could.
reddit = praw.Reddit(client_id = cid, client_secret = secret,
password = password, user_agent = user_agent, username = username,)
print(reddit.read_only)
False
Let's get a Subreddit. Let's say r/memes. For r/memes we'll retrieve the display name and the title. The greyed out comment is to get the subreddit description.
# assume you have a praw.Reddit instance bound to variable `reddit`
subreddit = reddit.subreddit("memes")
print(subreddit.display_name)
print(subreddit.title)
#print(subreddit.description)
memes /r/Memes the original since 2008
Now that we have a subreddit instance, let's get some post information within that subreddit! I'll place the search results into lists that can later go into a pandas dataframe by zipping them together :]
titles = []
scores = []
ids = []
urls = []
# assume you have a Subreddit instance bound to variable `subreddit`
for submission in subreddit.hot(limit=10):
#print(submission.title)
#print(submission.score)
#print(submission.id)
#print(submission.url)
titles.append(submission.title)
scores.append(submission.score)
ids.append(submission.id)
urls.append(submission.url)
col_names =['title','score','id','url']
posts = list(zip(titles, scores, ids, urls))
reddit_df = pd.DataFrame(posts, columns = col_names)
reddit_df
| title | score | id | url | |
|---|---|---|---|---|
| 0 | Gifs have been enabled in the comments for thi... | 478 | wfd0ji | https://www.reddit.com/r/memes/comments/wfd0ji... |
| 1 | Pure anarchy | 18127 | wrstv0 | https://i.redd.it/us9coxh00ji91.jpg |
| 2 | Makes Sense | 4270 | wrx6yz | https://i.redd.it/0968es6ovji91.jpg |
| 3 | I'm the chosen one | 8817 | wru9r9 | https://i.redd.it/a0uxvoycaji91.gif |
| 4 | The urban legend of Wanksy | 9069 | wrt1f5 | https://i.redd.it/4f343t4k1ji91.jpg |
| 5 | double the d's | 979 | ws3ew6 | https://i.redd.it/274x770jbli91.jpg |
| 6 | Spanking then monkey amiright | 2424 | wrwrwo | https://i.redd.it/nmiidr3hsji91.gif |
| 7 | You gotta fight! For your right! To POTTY! - [... | 1635 | wryro5 | https://i.redd.it/o4z5zqn08ki91.jpg |
| 8 | That doesn't narrow it down... | 11375 | wro4c3 | https://i.redd.it/80223ljx1ii91.png |
| 9 | truly remarkable | 2098 | wrvpzn | https://i.redd.it/249qs5prkji91.jpg |
We can get more information like instances of specific Redditors, Comments and Submissions. The data should be able to be handled in a similar way as above so it's a simple matter of formatting the query correctly. I'll see what project I can carry out using this.
I included a variety of ways to do things here ranging from performing simple, single queries to parsing data from deeply nested json files to performing multiple queries to compile sufficiently large datasets.
Happy data gathering!